{"as_of":"2026-08-08T11:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:82ade48de863442d18945efad3fe79ffa9fb6329291d33c63bd301eaefc44076","coverage":[{"denominator":95,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":95,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:56:03.866876Z","state":"measured"},{"denominator":95,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":95,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.09562/citation-record","integrity":"/paper/2507.09562/integrity","json":"/paper/2507.09562/citation-record.json","paper":"/paper/2507.09562"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.532961Z","title":"BioSAM: Generating SAM Prompts From Superpixel Graph for Biological In- stance Segmentation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.532961Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:12b30e4b7ba42e0c3e2b0b0d27f2bc060baa03aff1142da4d32ceeab9695df6a","observation_id":"15ad3b6f-bcf1-40fd-ba1a-3cfe858c8377","resolution":{"observed_at":"2026-08-06T17:56:03.532961Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.35361","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:05.214136Z","title":"Personalizing Vision-Language Models With Hybrid Prompts for Zero-Shot Anomaly Detection","venue":null,"work_id":"5ce212fd-abe2-4991-b08c-4fbcdbe632cd","year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.537564Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:5b9746754d0c1d5d25393f7e3d5e874587898685684dba18449218b2f046f199","observation_id":"436c2634-dac8-4417-a980-aaf8aac02935","resolution":{"observed_at":"2026-08-06T17:56:05.220660Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.542413Z","title":"RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.542413Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:0ce3eaab7496ff4beecafab5bcb8e76267863846f0b6f4470322e7bc4d63d04b","observation_id":"21cc5df1-8f53-48ed-b5b3-228a34ffca1a","resolution":{"observed_at":"2026-08-06T17:56:03.542413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07183","last_updated":"2024-03-20T06:42:18Z","snapshot_observed_at":"2026-07-06T16:30:55.043821Z","submitted_at":"2023-10-11T04:14:59Z","title":"SAM-OCTA: Prompting Segment-Anything for OCTA Image Segmentation","version":2},"cited_work":{"arxiv_id":"2310.07183","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.07183","snapshot_observed_at":"2026-08-06T17:56:05.136796Z","title":"SAM-OCTA: Prompting Segment-Anything for OCTA Image Segmentation","venue":"cs.LG","work_id":"5799bced-f280-4d7f-8516-ab389e7f6f5f","year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.545980Z"},"links":{"cited_paper":"/paper/2310.07183","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:362f27e9cc3c178610f1c4ca5892305bc360510d47dddf8af34fc434b6139b5e","observation_id":"d37862e8-26e5-4aac-b56c-7de7f0e5a73e","resolution":{"observed_at":"2026-08-06T17:56:05.140111Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.550396Z","title":"Segmentation by registration-enabled SAM prompt engineering using five reference images","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.550396Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:776c3020007c9d20ba58230c306a786dd7f39d6c0fe55b9b8c49f89f44260fac","observation_id":"14690656-2672-4117-ae62-d01f27879b63","resolution":{"observed_at":"2026-08-06T17:56:03.550396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.00290","last_updated":"2023-08-29T03:31:58Z","snapshot_observed_at":"2026-07-06T15:49:11.121894Z","submitted_at":"2023-07-01T10:12:46Z","title":"All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning","version":2},"cited_work":{"arxiv_id":"2307.00290","doi":null,"metadata_source":"pith","pith_arxiv_id":"2307.00290","snapshot_observed_at":"2026-08-06T17:56:05.108061Z","title":"All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning","venue":"cs.CV","work_id":"d2cd2a2f-c83c-47f6-a187-60d191849253","year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.558357Z"},"links":{"cited_paper":"/paper/2307.00290","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:d9feefef598f239c26993b3255b15480a97ba293b1c281ede9ea711443a474a9","observation_id":"7ea8613b-864a-461f-86bc-a2e50a123db4","resolution":{"observed_at":"2026-08-06T17:56:05.112400Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01187","last_updated":"2024-03-19T12:27:37Z","snapshot_observed_at":"2026-07-06T15:49:47.586513Z","submitted_at":"2023-07-03T17:52:44Z","title":"SAMAug: Point Prompt Augmentation for Segment Anything Model","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01187","snapshot_observed_at":"2026-08-06T17:56:03.562790Z","title":"SAMAug: Point Prompt Augmenta- tion for Segment Anything Model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.562790Z"},"links":{"cited_paper":"/paper/2307.01187","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:59a2b5a695c6d5c2d25fed00cf0735c6248e40ffea22525d148fdacd39685a73","observation_id":"4595d20f-a178-4441-b39f-9a356ffca704","resolution":{"observed_at":"2026-08-06T17:56:03.562790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.11998","last_updated":"2024-04-18T08:46:12Z","snapshot_observed_at":"2026-07-06T18:02:02.384288Z","submitted_at":"2024-04-18T08:46:12Z","title":"Curriculum Point Prompting for Weakly-Supervised Referring Image Segmentation","version":1},"cited_work":{"arxiv_id":"2404.11998","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.11998","snapshot_observed_at":"2026-08-06T17:56:05.083820Z","title":"Curriculum Point Prompting for Weakly-Supervised Referring Image Segmentation","venue":"cs.CV","work_id":"58d223ee-889b-42ba-a651-a303c880831f","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.567186Z"},"links":{"cited_paper":"/paper/2404.11998","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:62e523e3d4e878209fbd522efe76977f7f9ebedfdf8690946c6f6a40d75cb67a","observation_id":"a731a88c-e06f-42f2-b7fa-5bdd12cb1df7","resolution":{"observed_at":"2026-08-06T17:56:05.087584Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.04973","last_updated":"2023-07-11T02:27:45Z","snapshot_observed_at":"2026-07-06T15:52:24.803146Z","submitted_at":"2023-07-11T02:27:45Z","title":"SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image","version":1},"cited_work":{"arxiv_id":"2307.04973","doi":null,"metadata_source":"pith","pith_arxiv_id":"2307.04973","snapshot_observed_at":"2026-08-06T17:56:05.069760Z","title":"SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image","venue":"cs.CV","work_id":"ef79dec3-a384-439f-849c-e4d829a3f3ac","year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.571150Z"},"links":{"cited_paper":"/paper/2307.04973","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:e9078097d7e8ccf6510fbcf9abcd3df41435427d6a9baa3ba49d4c600daa7d40","observation_id":"84889892-16e0-4231-a0d6-2c02d2d28ebe","resolution":{"observed_at":"2026-08-06T17:56:05.073852Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06825","last_updated":"2024-10-09T12:37:12Z","snapshot_observed_at":"2026-07-06T19:30:21.486298Z","submitted_at":"2024-10-09T12:37:12Z","title":"K-SAM: A Prompting Method Using Pretrained U-Net to Improve Zero Shot Performance of SAM on Lung Segmentation in CXR Images","version":1},"cited_work":{"arxiv_id":"2410.06825","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.06825","snapshot_observed_at":"2026-08-06T17:56:05.056170Z","title":"K-SAM: A Prompting Method Using Pretrained U-Net to Improve Zero Shot Performance of SAM on Lung Segmentation in CXR Images","venue":"eess.IV","work_id":"a1bc02f7-f356-45bf-b9f7-0b1def7a1853","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.574930Z"},"links":{"cited_paper":"/paper/2410.06825","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:42d8c921694c2394b5dae6de851f5589483a27463a49c4a89981a04a23969713","observation_id":"6089519b-2b26-4acb-a0c0-fea62ce03576","resolution":{"observed_at":"2026-08-06T17:56:05.059830Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.20293","last_updated":"2024-09-30T13:53:01Z","snapshot_observed_at":"2026-07-06T19:24:35.007698Z","submitted_at":"2024-09-30T13:53:01Z","title":"Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision","version":1},"cited_work":{"arxiv_id":"2409.20293","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.20293","snapshot_observed_at":"2026-08-06T17:56:05.042273Z","title":"Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision","venue":"cs.CV","work_id":"9acd5d82-80fa-46e2-a345-66a95d146dc5","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.578785Z"},"links":{"cited_paper":"/paper/2409.20293","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:e69aa1cdec1c42982fd18cd87397addf8eebb38faa257c004e20c32ecc0bf92f","observation_id":"15997316-3d74-47b0-b62e-c9247c205b2d","resolution":{"observed_at":"2026-08-06T17:56:05.045944Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.07172","last_updated":"2024-09-11T10:35:42Z","snapshot_observed_at":"2026-07-06T19:13:42.074230Z","submitted_at":"2024-09-11T10:35:42Z","title":"Swin-LiteMedSAM: A Lightweight Box-Based Segment Anything Model for Large-Scale Medical Image Datasets","version":1},"cited_work":{"arxiv_id":"2409.07172","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.07172","snapshot_observed_at":"2026-08-06T17:56:05.027643Z","title":"Swin-LiteMedSAM: A Lightweight Box-Based Segment Anything Model for Large-Scale Medical Image Datasets","venue":"cs.CV","work_id":"490ec65c-b3e7-403d-a2d1-3992ae127319","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.582093Z"},"links":{"cited_paper":"/paper/2409.07172","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:8f6761f984a925e9dfc57d43f998bd496b682d4e26107871a817a169fa305c64","observation_id":"d9b587d8-d065-4da3-8b59-306c29690b89","resolution":{"observed_at":"2026-08-06T17:56:05.031591Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.585220Z","title":"Lite Class-Prompt Tiny-VIT for Multi-modality Medical Image Segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.585220Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:0fd8c2015818662767673e019bebe85f8d63aa578df168e8bb87a7eb5fc487e2","observation_id":"12a1a84e-2992-47d4-8179-b14f251ea585","resolution":{"observed_at":"2026-08-06T17:56:03.585220Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.588317Z","title":"APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.588317Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:51e3acd419bc15e4791527feef39c4dcc664cd3f51a76b543f6d84bd1869eca6","observation_id":"e4d0af7c-3cd0-422a-b0ca-d208cf8c9bec","resolution":{"observed_at":"2026-08-06T17:56:03.588317Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.594919Z","title":"Relax Image-Specific Prompt Require- ment in SAM: A Single Generic Prompt for Segment- ing Camouflaged Objects","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.594919Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:bf3fe3ae5cf0d93d28c37ac91cc01745e1c45b278293d17e7c703ab56010f02e","observation_id":"567284f3-253c-420a-9727-cb72e63bd824","resolution":{"observed_at":"2026-08-06T17:56:03.594919Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08372","last_updated":"2024-06-13T03:10:17Z","snapshot_observed_at":"2026-08-05T14:22:17.508106Z","submitted_at":"2024-06-12T16:20:58Z","title":"APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation","version":2},"cited_work":{"arxiv_id":"2406.08372","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.08372","snapshot_observed_at":"2026-08-06T17:56:05.011348Z","title":"APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation","venue":"cs.CV","work_id":"38e282fb-b075-4645-a8f4-76c0eae79f07","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.591406Z"},"links":{"cited_paper":"/paper/2406.08372","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:effb5a5b7855a82a5b22b1e4892705c76acfb70cb7c20695068c4e2bd39cb3c7","observation_id":"4108d9f1-12bb-41ec-bcd1-7a9a68f854fa","resolution":{"observed_at":"2026-08-06T17:56:05.015659Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.06817","last_updated":"2025-04-16T03:40:29Z","snapshot_observed_at":"2026-07-06T20:34:11.407726Z","submitted_at":"2025-02-05T03:08:17Z","title":"Diffusion-empowered AutoPrompt MedSAM","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.06817","snapshot_observed_at":"2026-08-06T17:56:03.602547Z","title":"Diffusion-empowered AutoPrompt MedSAM","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.602547Z"},"links":{"cited_paper":"/paper/2502.06817","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:9547e5ac012dd85fcef20bd19dfee514f5bf6399ced4e55a66a2c1348d60087f","observation_id":"a04d7fa8-bb61-4ed6-8f56-1259d326a78a","resolution":{"observed_at":"2026-08-06T17:56:03.602547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04651","last_updated":"2024-01-09T16:24:25Z","snapshot_observed_at":"2026-08-04T14:50:11.565250Z","submitted_at":"2024-01-09T16:24:25Z","title":"Learning to Prompt Segment Anything Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04651","snapshot_observed_at":"2026-08-06T17:56:03.598319Z","title":"Learning to Prompt Segment Anything Models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.598319Z"},"links":{"cited_paper":"/paper/2401.04651","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:3e285d9ebcacf194d29f88dbb214753046868d67486844a8bc0e5b3eec2c91dc","observation_id":"fcaae867-d249-48a2-99f8-088839477017","resolution":{"observed_at":"2026-08-06T17:56:03.598319Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21284","last_updated":"2024-07-31T02:16:28Z","snapshot_observed_at":"2026-07-06T18:54:49.923926Z","submitted_at":"2024-07-31T02:16:28Z","title":"Robust Box Prompt based SAM for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":"2407.21284","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.21284","snapshot_observed_at":"2026-08-06T17:56:04.978984Z","title":"Robust Box Prompt based SAM for Medical Image Segmentation","venue":"cs.CV","work_id":"cf0d6c9f-47cf-45b5-8c12-57aa84e682cf","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.610184Z"},"links":{"cited_paper":"/paper/2407.21284","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:3ab7edf111f788c6bc779ec6f8cfc5f52479690f7a45101bc70f42046e6ac5c6","observation_id":"c2903221-8b7a-405f-abc0-1c8b44a12666","resolution":{"observed_at":"2026-08-06T17:56:04.982942Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.606107Z","title":"Optimizing Efficiency and Effec- tiveness in Sequential Prompt Strategy for SAM us- ing Reinforcement Learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.606107Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:b5b0e5c6281769f5abb2c8f4e9b43a7463805bfac104cb6d5f4a4eff09e93f09","observation_id":"cd32f2aa-e2a2-4c26-acec-61f41f27ba2e","resolution":{"observed_at":"2026-08-06T17:56:03.606107Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.12781","last_updated":"2025-03-17T03:33:36Z","snapshot_observed_at":"2026-08-08T11:36:26.286106Z","submitted_at":"2025-03-17T03:33:36Z","title":"SAM2 for Image and Video Segmentation: A Comprehensive Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.12781","snapshot_observed_at":"2026-08-06T17:56:03.617338Z","title":"SAM2 for Image and Video Segmentation: A Comprehensive Survey","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.617338Z"},"links":{"cited_paper":"/paper/2503.12781","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:5da52439e78eb8d7daeb343bc1442d909d9e9a2e424f0fb169ffef63a7637467","observation_id":"289f5e13-0aa5-4ce2-9648-8522fff6928f","resolution":{"observed_at":"2026-08-06T17:56:03.617338Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.15759","last_updated":"2024-10-14T14:41:52Z","snapshot_observed_at":"2026-07-06T17:34:53.153463Z","submitted_at":"2024-02-24T08:10:54Z","title":"TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation","version":2},"cited_work":{"arxiv_id":"2402.15759","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.15759","snapshot_observed_at":"2026-08-06T17:56:04.964082Z","title":"TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation","venue":"cs.CV","work_id":"38949b45-64ce-4239-8420-834b8495612d","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.613836Z"},"links":{"cited_paper":"/paper/2402.15759","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:cc4f0374f0303bdc3ecd900c24602bfaafb8c3836169a590e9bfde6a5d196d63","observation_id":"b112e6ad-4eaf-4e5b-86ad-89e480845df2","resolution":{"observed_at":"2026-08-06T17:56:04.967828Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.02643","last_updated":"2023-04-05T17:59:46Z","snapshot_observed_at":"2026-08-08T05:14:59.435033Z","submitted_at":"2023-04-05T17:59:46Z","title":"Segment Anything","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.02643","snapshot_observed_at":"2026-08-06T17:56:03.625174Z","title":"Segment Anything","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.625174Z"},"links":{"cited_paper":"/paper/2304.02643","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:7fa0f9e8496dd0b719b60445bcf6d400309ad173c69f413356c6eab07bf767ad","observation_id":"ade55baa-af27-45b6-85dd-1fcaa6b0f58d","resolution":{"observed_at":"2026-08-06T17:56:03.625174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.621306Z","title":"Ul- tralytics YOLOv8","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.621306Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:41f266fcf8f91372786c5b43f073e51d8cb581bd008183b1b27db34511bda596","observation_id":"3735bddb-bff8-4e63-b979-5b2c9c7c4868","resolution":{"observed_at":"2026-08-06T17:56:03.621306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.03857","last_updated":"2022-06-17T10:32:21Z","snapshot_observed_at":"2026-07-06T12:16:15.735863Z","submitted_at":"2021-12-07T17:47:50Z","title":"Grounded Language-Image Pre-training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.03857","snapshot_observed_at":"2026-08-06T17:56:03.635888Z","title":"Grounded Language-Image Pre-training","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.635888Z"},"links":{"cited_paper":"/paper/2112.03857","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:b686a89a29abb684225cba6c2f99f67daa43b7427717a681dd375cc619ed883d","observation_id":"88b9d180-4932-4cf0-aa7f-acb343543d6e","resolution":{"observed_at":"2026-08-06T17:56:03.635888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.628800Z","title":"AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.628800Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:5d18d7507047d8a331959a3795a655e56fbe22310c35675cbbbfbcff1bf8164e","observation_id":"32749c5c-320b-4e7d-af05-98f4bbffe3cb","resolution":{"observed_at":"2026-08-06T17:56:03.628800Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.05653","last_updated":"2024-09-16T09:10:00Z","snapshot_observed_at":"2026-07-06T15:14:44.015806Z","submitted_at":"2023-04-12T07:16:55Z","title":"A Closer Look at the Explainability of Contrastive Language-Image Pre-training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.05653","snapshot_observed_at":"2026-08-06T17:56:03.642271Z","title":"A Closer Look at the Explainability of Con- trastive Language-Image Pre-training","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.642271Z"},"links":{"cited_paper":"/paper/2304.05653","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:982abbaa6ea944f269074384c51aa07c359fedc971e3c4969b4dcbb78da204b8","observation_id":"5ea382ed-e060-4ca9-8108-5dab0216cfab","resolution":{"observed_at":"2026-08-06T17:56:03.642271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2410.09714","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:04.848176Z","title":"AM-SAM: Automated Prompting and Mask Calibration for Segment Anything Model","venue":null,"work_id":"98e9b001-41b3-4046-a938-5e054a7be83c","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.645375Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:2432882ea855a0377e5674e8a2015bf82ace5707342c03dba02e9daf9888f330","observation_id":"a03b3165-40be-40ee-97c9-7a4762714936","resolution":{"observed_at":"2026-08-06T17:56:04.853660Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.639428Z","title":"ClipSAM: CLIP and SAM collabo- ration for zero-shot anomaly segmentation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.639428Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:799664eedabb9a9332d50aa07801ac2816c1d50f4356d18585c430d37a29cd02","observation_id":"6615a959-794e-4b0b-828e-02ede25f15c8","resolution":{"observed_at":"2026-08-06T17:56:03.639428Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.06756","last_updated":"2025-03-16T10:12:23Z","snapshot_observed_at":"2026-08-06T06:08:06.089475Z","submitted_at":"2025-02-10T18:33:15Z","title":"SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.06756","snapshot_observed_at":"2026-08-06T17:56:03.652348Z","title":"SAMRefiner: Taming Segment Any- thing Model for Universal Mask Refinement","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.652348Z"},"links":{"cited_paper":"/paper/2502.06756","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:33786feeedebd381289e2f3495bd02f8821e3c3ef2d719a7d37636ca4dae8597","observation_id":"2acd78b7-37ca-432a-bf77-e750333dcadb","resolution":{"observed_at":"2026-08-06T17:56:03.652348Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.655364Z","title":"Training- Free Open-Ended Object Detection and Segmentation via Attention as Prompts","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.655364Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:3f17b9ab9a1d8e10669daddf826ceed2eb9078dffe4504a31de2190512052838","observation_id":"45ec1059-57d9-439a-98e3-ec592b5863db","resolution":{"observed_at":"2026-08-06T17:56:03.655364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.13258","last_updated":"2024-03-20T02:39:15Z","snapshot_observed_at":"2026-07-06T17:47:26.534826Z","submitted_at":"2024-03-20T02:39:15Z","title":"SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts","version":1},"cited_work":{"arxiv_id":"2403.13258","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.13258","snapshot_observed_at":"2026-08-06T17:56:04.772619Z","title":"SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts","venue":"cs.CV","work_id":"7e17581b-eca3-4c9e-874c-22545074ae01","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.648451Z"},"links":{"cited_paper":"/paper/2403.13258","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:70c4c6124a94e836607026ce6f579be334e03366f519fccfbae5ebe0fc6f8cc6","observation_id":"5218031c-c5ee-477a-a71d-f6dc6c057fe1","resolution":{"observed_at":"2026-08-06T17:56:04.777374Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00741","last_updated":"2024-03-31T17:02:24Z","snapshot_observed_at":"2026-07-06T17:53:44.044640Z","submitted_at":"2024-03-31T17:02:24Z","title":"Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Prompts","version":1},"cited_work":{"arxiv_id":"2404.00741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.00741","snapshot_observed_at":"2026-08-06T17:56:04.731972Z","title":"Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Prompts","venue":"cs.CV","work_id":"46daa3b7-e820-4c15-818d-c1835abccc40","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.662871Z"},"links":{"cited_paper":"/paper/2404.00741","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:75191541c4341e2769727d64145134fb6795d10280adb121020667a8c098b437","observation_id":"e8731712-80b3-4052-b17e-4d8817514c62","resolution":{"observed_at":"2026-08-06T17:56:04.736805Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.05499","last_updated":"2024-07-19T06:00:41Z","snapshot_observed_at":"2026-07-06T15:00:58.804337Z","submitted_at":"2023-03-09T18:52:16Z","title":"Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.05499","snapshot_observed_at":"2026-08-06T17:56:03.667197Z","title":"Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object De- tection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.667197Z"},"links":{"cited_paper":"/paper/2303.05499","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:3b242f839d13db7ed63bf9b1f52fef0ef83e019fb8ec68980e61c5cc7bf95037","observation_id":"b47fcfa1-a7ef-49ad-affa-122558501eb0","resolution":{"observed_at":"2026-08-06T17:56:03.667197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.01010","last_updated":"2024-01-02T03:37:11Z","snapshot_observed_at":"2026-07-06T17:10:39.974230Z","submitted_at":"2024-01-02T03:37:11Z","title":"Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt","version":1},"cited_work":{"arxiv_id":"2401.01010","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.01010","snapshot_observed_at":"2026-08-06T17:56:04.747813Z","title":"Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt","venue":"cs.CV","work_id":"f5c81e69-3d40-488b-bf30-8a9297d915d2","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.659116Z"},"links":{"cited_paper":"/paper/2401.01010","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:37e1d735d2a70b679295d4265c1808851bce22f33dfeacc1f9af25d1046626ba","observation_id":"4370c5d7-1f32-4048-b13a-aa88c07f8ed0","resolution":{"observed_at":"2026-08-06T17:56:04.751899Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16271","last_updated":"2024-06-24T02:33:46Z","snapshot_observed_at":"2026-07-06T18:35:45.699877Z","submitted_at":"2024-06-24T02:33:46Z","title":"Feature-prompting GBMSeg: One-Shot Reference Guided Training-Free Prompt Engineering for Glomerular Basement Membrane Segmentation","version":1},"cited_work":{"arxiv_id":"2406.16271","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.16271","snapshot_observed_at":"2026-08-06T17:56:04.691881Z","title":"Feature-prompting GBMSeg: One-Shot Reference Guided Training-Free Prompt Engineering for Glomerular Basement Membrane Segmentation","venue":"cs.CV","work_id":"0c96344b-8b1a-4835-a654-404c3f3a3ea6","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.673842Z"},"links":{"cited_paper":"/paper/2406.16271","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:c9bc2ab7318b77a7cbafe221de9ef785de74f07ca02d32251bd81fab0e8cf087","observation_id":"82db0b4e-17c5-4ec3-98b3-fde59be56eab","resolution":{"observed_at":"2026-08-06T17:56:04.697030Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.677294Z","title":"SAM-RSIS: Progressively Adapt- ing SAM With Box Prompting to Remote Sensing Im- age Instance Segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.677294Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:ca44bf063062f4359f16560474903424604e851ff4226e77e961a5b676e5708b","observation_id":"a39dcf39-ed16-42bb-b23f-78ec5dc83d2d","resolution":{"observed_at":"2026-08-06T17:56:03.677294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00706","last_updated":"2024-08-01T16:52:39Z","snapshot_observed_at":"2026-07-06T18:55:41.459417Z","submitted_at":"2024-08-01T16:52:39Z","title":"Point-supervised Brain Tumor Segmentation with Box-prompted MedSAM","version":1},"cited_work":{"arxiv_id":"2408.00706","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.00706","snapshot_observed_at":"2026-08-06T17:56:04.707510Z","title":"Point-supervised Brain Tumor Segmentation with Box-prompted MedSAM","venue":"cs.CV","work_id":"7ce71cf9-dfc1-4444-8f39-5c34ca3c4edb","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.670414Z"},"links":{"cited_paper":"/paper/2408.00706","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:34075d4402a5378d1476d4fe6b8c8a4e7ace3b8bdc15b500d6e8572e4fb2f1d3","observation_id":"23782cd2-f165-4de0-8f7a-b804dcce4a1f","resolution":{"observed_at":"2026-08-06T17:56:04.711231Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16246","last_updated":"2025-01-27T17:43:51Z","snapshot_observed_at":"2026-07-06T20:26:50.822135Z","submitted_at":"2025-01-27T17:43:51Z","title":"CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentation","version":1},"cited_work":{"arxiv_id":"2501.16246","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.16246","snapshot_observed_at":"2026-08-06T17:56:04.519377Z","title":"CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentation","venue":"cs.CV","work_id":"d75d7946-af51-49c6-974b-bf1f8de8afd4","year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.686063Z"},"links":{"cited_paper":"/paper/2501.16246","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:811e4de9bf1b47e33e566b29f4b9369d42ce0dcf3aa8f7e11a92422bb4609d03","observation_id":"604e03be-f9b1-4d51-a5ba-8b31db9a6103","resolution":{"observed_at":"2026-08-06T17:56:04.523720Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.09484","last_updated":"2024-09-14T17:11:37Z","snapshot_observed_at":"2026-07-06T19:15:25.798160Z","submitted_at":"2024-09-14T17:11:37Z","title":"Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model","version":1},"cited_work":{"arxiv_id":"2409.09484","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.09484","snapshot_observed_at":"2026-08-06T17:56:04.502152Z","title":"Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model","venue":"eess.IV","work_id":"5587207c-5a5e-4661-b28d-1c999f230d92","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.689653Z"},"links":{"cited_paper":"/paper/2409.09484","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:a301479673001bdebc2e75220c71007ea212febc5cff662babc73f2805444208","observation_id":"0756f776-404d-4cdb-9da9-1c80605c9b28","resolution":{"observed_at":"2026-08-06T17:56:04.507057Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.680295Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.680295Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:7ca6111c193357f7494ee7b6d0fb166ebc03153d8ac0a4211b6d5cfaede5c93c","observation_id":"f89d6282-715d-4bff-bfb1-0c0fbb06fc71","resolution":{"observed_at":"2026-08-06T17:56:03.680295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.683298Z","title":"GroupPrompter: A Prompting Method for Semantic Segmentation Based on SAM","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.683298Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:685a8b91c06cf3826a10ebb2d8808fc53a78a4c76506aedc73c741e3fed3388b","observation_id":"fddd6059-67d3-470d-b596-797a21b563b1","resolution":{"observed_at":"2026-08-06T17:56:03.683298Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.01538","last_updated":"2021-10-14T18:27:04Z","snapshot_observed_at":"2026-07-06T10:56:19.759252Z","submitted_at":"2021-04-04T05:27:13Z","title":"Hypercorrelation Squeeze for Few-Shot Segmentation","version":3},"cited_work":{"arxiv_id":"2104.01538","doi":null,"metadata_source":"pith","pith_arxiv_id":"2104.01538","snapshot_observed_at":"2026-08-06T17:56:04.472497Z","title":"Hypercorrelation Squeeze for Few-Shot Segmentation","venue":"cs.CV","work_id":"4f03fa4f-f15f-4a36-986f-7f907184213a","year":2021},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.700858Z"},"links":{"cited_paper":"/paper/2104.01538","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:d137ae95ceb5a483f86253c5639433dd4a033e01d92bb76f806d4ae0a04fd0ce","observation_id":"fdeabbb9-def9-4d37-9bfd-f5a0d34aaee8","resolution":{"observed_at":"2026-08-06T17:56:04.476087Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06795","last_updated":"2025-07-15T09:10:02Z","snapshot_observed_at":"2026-07-06T18:43:38.955120Z","submitted_at":"2024-07-09T12:08:07Z","title":"CycleSAM: Few-Shot Surgical Scene Segmentation with Cycle- and Scene-Consistent Feature Matching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06795","snapshot_observed_at":"2026-08-06T17:56:03.704048Z","title":"CycleSAM: One-Shot Surgical Scene Segmentation using Cycle-Consistent Feature Matching to Prompt SAM","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.704048Z"},"links":{"cited_paper":"/paper/2407.06795","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:647f46033d287aa378cffb54e07ff08bad9a5187f758c608d5e93b439f20dca5","observation_id":"4b9185cd-8d8e-427e-ae0f-cf21d77d3380","resolution":{"observed_at":"2026-08-06T17:56:03.704048Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.02075","last_updated":"2025-08-21T14:39:31Z","snapshot_observed_at":"2026-08-04T18:35:43.328961Z","submitted_at":"2024-07-02T09:08:06Z","title":"Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts","version":4},"cited_work":{"arxiv_id":"2407.02075","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.02075","snapshot_observed_at":"2026-08-06T17:56:04.487648Z","title":"Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts","venue":"cs.CV","work_id":"23315e1d-d899-46cf-a215-bddd05ab84b3","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.693316Z"},"links":{"cited_paper":"/paper/2407.02075","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:02923a0687557c4937bd976f16d6ba53975bd4f28f4217bfc4cdce831ee29f7c","observation_id":"91aa0fcc-e747-47cc-8e27-ac5d7377d437","resolution":{"observed_at":"2026-08-06T17:56:04.491236Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.697265Z","title":"Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medi- cal Image Segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.697265Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:12e3f1ed482f162f9635501f60b6e0db0d79a3f13ea6fb67e50836471b0ad235","observation_id":"aa9027e9-a4da-4d77-aceb-b497cbf9ed5e","resolution":{"observed_at":"2026-08-06T17:56:03.697265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.714928Z","title":"Benchmarking Human and Au- tomated Prompting in the Segment Anything Model","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.714928Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:0411f7647bdaa835b662aba30348f152605150d151a4aea782dc5df5011fc7e0","observation_id":"eb015687-a4e5-4da5-a72c-dfb8a592e292","resolution":{"observed_at":"2026-08-06T17:56:03.714928Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00020","last_updated":"2021-02-26T19:04:58Z","snapshot_observed_at":"2026-07-06T10:45:03.059688Z","submitted_at":"2021-02-26T19:04:58Z","title":"Learning Transferable Visual Models From Natural Language Supervision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.00020","snapshot_observed_at":"2026-08-06T17:56:03.723010Z","title":"Learning Transferable Visual Mod- els From Natural Language Supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.723010Z"},"links":{"cited_paper":"/paper/2103.00020","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:44d0a899056d4d4a5f8b138dee9af0ca585b0a1a84130f3e637250ed8fbbda15","observation_id":"14034ff9-b270-41bd-81ab-ddab08b7fac0","resolution":{"observed_at":"2026-08-06T17:56:03.723010Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.13220","last_updated":"2024-01-24T04:23:17Z","snapshot_observed_at":"2026-07-06T17:19:44.130417Z","submitted_at":"2024-01-24T04:23:17Z","title":"Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.13220","snapshot_observed_at":"2026-08-06T17:56:03.707859Z","title":"Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.707859Z"},"links":{"cited_paper":"/paper/2401.13220","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:20d979559267a562a0d82fe817324a084cf05de98886e3a253665de6d3426ae4","observation_id":"2288b4cb-de84-4c05-a836-9333a146de69","resolution":{"observed_at":"2026-08-06T17:56:03.707859Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.11998","last_updated":"2024-12-16T17:26:06Z","snapshot_observed_at":"2026-07-06T20:07:54.727859Z","submitted_at":"2024-12-16T17:26:06Z","title":"SAMIC: Segment Anything with In-Context Spatial Prompt Engineering","version":1},"cited_work":{"arxiv_id":"2412.11998","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.11998","snapshot_observed_at":"2026-08-06T17:56:04.439194Z","title":"SAMIC: Segment Anything with In-Context Spatial Prompt Engineering","venue":"cs.CV","work_id":"82009419-1026-4e76-90ba-fa10ffc2f999","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.711771Z"},"links":{"cited_paper":"/paper/2412.11998","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:4a4784fbf2a91ff0f49c3b3e721dd5ff928b2cf1ea1d7c9fd5f6e2095961c1a0","observation_id":"f5c9f04c-3cca-460c-9352-ba3d276a36bc","resolution":{"observed_at":"2026-08-06T17:56:04.443171Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.732813Z","title":"Temporally-Extended Prompts Optimization for SAM in Interactive Medical Im- age Segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.732813Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:d334a42af16916477975e3f4e85f9dfd3ced85ee50f106ee4193efd47178bc6e","observation_id":"75fd4680-1a32-44e2-b7a6-5da3903e592a","resolution":{"observed_at":"2026-08-06T17:56:03.732813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.22048","last_updated":"2024-10-30T18:51:38Z","snapshot_observed_at":"2026-08-02T01:07:20.177956Z","submitted_at":"2024-10-29T13:56:31Z","title":"Benchmarking Human and Automated Prompting in the Segment Anything Model","version":2},"cited_work":{"arxiv_id":"2410.22048","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.22048","snapshot_observed_at":"2026-08-06T17:56:04.423239Z","title":"Benchmarking Human and Automated Prompting in the Segment Anything Model","venue":"cs.CV","work_id":"2fe80ec5-c0a5-421d-8041-8f174f7a5183","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.718410Z"},"links":{"cited_paper":"/paper/2410.22048","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:8e76cb7dd8331a9447e3e60184744cc25d1ad7e2a32e77548c706c8f80c5d2db","observation_id":"24d0ad99-ef17-4717-9b2a-3cb5c8d330ba","resolution":{"observed_at":"2026-08-06T17:56:04.426913Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.739877Z","title":"EP-SAM: Weakly Supervised Histopathology Segmentation via Enhanced Prompt with Segment Anything","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.739877Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:32726ddbc61cf9bf279bfa9c731700e1d14a9f7e915bbd0b3ee7ab9941347fe8","observation_id":"03eefa40-662b-46e1-8b6a-c7671f9d633e","resolution":{"observed_at":"2026-08-06T17:56:03.739877Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16740","last_updated":"2024-05-27T01:13:01Z","snapshot_observed_at":"2026-07-06T18:20:14.021759Z","submitted_at":"2024-05-27T01:13:01Z","title":"PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation","version":1},"cited_work":{"arxiv_id":"2405.16740","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.16740","snapshot_observed_at":"2026-08-06T17:56:04.396729Z","title":"PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation","venue":"cs.CV","work_id":"f2c5809c-fe6f-4d88-8f9d-d6c2b6cc2e00","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.726393Z"},"links":{"cited_paper":"/paper/2405.16740","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:e8e0b19d33f117d90d4e7508705f2f81b5381243468f725c9c2a0903465320e6","observation_id":"2b84c285-32b4-4572-af13-8b110c880b48","resolution":{"observed_at":"2026-08-06T17:56:04.400579Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00719","last_updated":"2025-02-02T08:40:14Z","snapshot_observed_at":"2026-07-06T20:29:45.369233Z","submitted_at":"2025-02-02T08:40:14Z","title":"Vision and Language Reference Prompt into SAM for Few-shot Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00719","snapshot_observed_at":"2026-08-06T17:56:03.729432Z","title":"Vision and Language Reference Prompt into SAM for Few-shot Segmentation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.729432Z"},"links":{"cited_paper":"/paper/2502.00719","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:1d3e04ca65d1b94173a1fea70ffa26fbd0c83460b5c9a3e068c0d4163bf8dfa0","observation_id":"95cf200c-2baa-42b4-a440-6ceecb325b54","resolution":{"observed_at":"2026-08-06T17:56:03.729432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.750299Z","title":"Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.750299Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:59d53e5f00f9a34ecfd15557226e217a8f365d5fa552fb7b2fdeeedafcfa4655","observation_id":"1a6808a4-3766-4812-9a1e-7714c19f2d50","resolution":{"observed_at":"2026-08-06T17:56:03.750299Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12562","last_updated":"2024-10-16T13:38:01Z","snapshot_observed_at":"2026-08-06T04:53:19.272377Z","submitted_at":"2024-10-16T13:38:01Z","title":"Adaptive Prompt Learning with SAM for Few-shot Scanning Probe Microscope Image Segmentation","version":1},"cited_work":{"arxiv_id":"2410.12562","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.12562","snapshot_observed_at":"2026-08-06T17:56:04.318202Z","title":"Adaptive Prompt Learning with SAM for Few-shot Scanning Probe Microscope Image Segmentation","venue":"cs.CV","work_id":"98fd5523-0aa7-4870-8971-28a4f3d5fcc9","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.736080Z"},"links":{"cited_paper":"/paper/2410.12562","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:9dd1eef99d460af2c804978f6a2a3b821e998b04db8745d4f60592b841b05dcf","observation_id":"64963b31-13f7-469d-8933-d4f691d65deb","resolution":{"observed_at":"2026-08-06T17:56:04.321932Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.760452Z","title":"CogVLM: Visual Expert for Pre- trained Language Models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.760452Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:fc24226606fcb2fa50c227842d7ecdae014660c433e287e4e976e3e98944777a","observation_id":"fcc94408-edc4-4101-ae27-11c4c45e3098","resolution":{"observed_at":"2026-08-06T17:56:03.760452Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.09212","last_updated":"2019-02-25T11:55:28Z","snapshot_observed_at":"2026-08-07T10:40:09.140030Z","submitted_at":"2019-02-25T11:55:28Z","title":"Deep High-Resolution Representation Learning for Human Pose Estimation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.09212","snapshot_observed_at":"2026-08-06T17:56:03.743278Z","title":"Deep High-Resolution Representation Learning for Human Pose Estimation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.743278Z"},"links":{"cited_paper":"/paper/1902.09212","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:a017c8a9379e6cea3bf3dcafee727058de533ba5328bb22bc534b71ba6b551cc","observation_id":"42646093-a1c2-4d38-8545-8b951bcc61ef","resolution":{"observed_at":"2026-08-06T17:56:03.743278Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04960","last_updated":"2026-06-04T11:59:03Z","snapshot_observed_at":"2026-08-02T12:51:51.839797Z","submitted_at":"2024-10-07T11:59:54Z","title":"On Efficient Variants of Segment Anything Model: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.04960","snapshot_observed_at":"2026-08-06T17:56:03.746472Z","title":"On Efficient Variants of Segment Anything Model: A Survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.746472Z"},"links":{"cited_paper":"/paper/2410.04960","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:bde29d429c89d1045dc93b5da8296f07da630149d85e927b73102a1e0e26454e","observation_id":"a5d8743d-c446-4aa2-95c8-d8c46d9a0dae","resolution":{"observed_at":"2026-08-06T17:56:03.746472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.10666","last_updated":"2022-07-21T17:59:56Z","snapshot_observed_at":"2026-07-06T13:33:54.916595Z","submitted_at":"2022-07-21T17:59:56Z","title":"TinyViT: Fast Pretraining Distillation for Small Vision Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.10666","snapshot_observed_at":"2026-08-06T17:56:03.770740Z","title":"TinyViT: Fast Pretraining Distillation for Small Vision Transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.770740Z"},"links":{"cited_paper":"/paper/2207.10666","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:23f0d5bc29fd7b09b2e561dfa7b01319fa88e0bb03b8164aee807da8f5a57fd2","observation_id":"e8bc9b41-a176-45d1-a94b-bf0ef87e5a4a","resolution":{"observed_at":"2026-08-06T17:56:03.770740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.06821","last_updated":"2024-09-10T19:04:44Z","snapshot_observed_at":"2026-07-06T19:13:24.690465Z","submitted_at":"2024-09-10T19:04:44Z","title":"Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts","version":1},"cited_work":{"arxiv_id":"2409.06821","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.06821","snapshot_observed_at":"2026-08-06T17:56:04.283753Z","title":"Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts","venue":"cs.CV","work_id":"64ef45f9-d7c8-4a9d-acb2-a87059477b7f","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.753689Z"},"links":{"cited_paper":"/paper/2409.06821","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:a0bbb08c66b83bff77edbe0efe929a0e01213589f66dbd7d20fcf3372dcb0686","observation_id":"c943913a-4cec-4eda-a1cc-6b441129a33e","resolution":{"observed_at":"2026-08-06T17:56:04.287615Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.13426","last_updated":"2025-05-22T02:03:01Z","snapshot_observed_at":"2026-07-06T20:24:50.262766Z","submitted_at":"2025-01-23T07:08:48Z","title":"Auto-Prompting SAM for Weakly Supervised Landslide Extraction","version":2},"cited_work":{"arxiv_id":"2501.13426","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.13426","snapshot_observed_at":"2026-08-06T17:56:04.269367Z","title":"Auto-Prompting SAM for Weakly Supervised Landslide Extraction","venue":"cs.CV","work_id":"87bc87bd-a796-414e-8256-e306f55b9392","year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.757241Z"},"links":{"cited_paper":"/paper/2501.13426","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:bfeb9524b15671fb33c9646bfa9279abd91121fcb93c32e12f79cda5fd94da2d","observation_id":"fdebbcd3-9203-48e1-a8e5-7d035ef15139","resolution":{"observed_at":"2026-08-06T17:56:04.273086Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:05.327003Z","title":"Self-Prompt SAM: Medical Image Seg- mentation via Automatic Prompt SAM Adaptation","venue":null,"work_id":"3bf02819-5714-47e2-bec0-4b0c768e51c3","year":null},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.779668Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:97dcf4471878f883ecc4d0bee0a8e3299c6a6f1cba94c89416661c3f2daab7c9","observation_id":"bdc40d2b-fd8d-4adc-8a55-02889471cecb","resolution":{"observed_at":"2026-08-06T17:56:05.331248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.07205","last_updated":"2025-03-11T12:55:57Z","snapshot_observed_at":"2026-07-06T20:04:25.198952Z","submitted_at":"2024-12-10T05:50:50Z","title":"CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices","version":3},"cited_work":{"arxiv_id":"2412.07205","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.07205","snapshot_observed_at":"2026-08-06T17:56:04.255913Z","title":"CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices","venue":"cs.CV","work_id":"5d02c6a7-6e34-4a52-bacb-ce5b6583095c","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.763652Z"},"links":{"cited_paper":"/paper/2412.07205","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:4de4b4e8771d4e59f9b44463bfe799e06199b26d46718d51b311a43bd79c0c59","observation_id":"78dd3b14-746a-4886-92de-99dcaa8b78ec","resolution":{"observed_at":"2026-08-06T17:56:04.259814Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17943","last_updated":"2024-12-28T15:39:00Z","snapshot_observed_at":"2026-07-06T20:12:26.927930Z","submitted_at":"2024-12-23T19:54:42Z","title":"Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities","version":2},"cited_work":{"arxiv_id":"2412.17943","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.17943","snapshot_observed_at":"2026-08-06T17:56:04.240695Z","title":"Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities","venue":"eess.IV","work_id":"77fea406-25f6-4769-829b-adc4ee0f5503","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.767394Z"},"links":{"cited_paper":"/paper/2412.17943","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:8ef78f753c22d229cdc37e2d436486bca89eb6d5b800dd42b4b268c74c8280a3","observation_id":"72d8c4a3-2589-4c46-b721-73249c6ecfcf","resolution":{"observed_at":"2026-08-06T17:56:04.244527Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14153","last_updated":"2025-07-01T04:07:15Z","snapshot_observed_at":"2026-07-06T18:48:53.833921Z","submitted_at":"2024-07-19T09:32:30Z","title":"De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14153","snapshot_observed_at":"2026-08-06T17:56:03.792775Z","title":"ESP-MedSAM: Efficient Self- Prompting SAM for Universal Domain-Generalized Medical Image Segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.792775Z"},"links":{"cited_paper":"/paper/2407.14153","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:51e683949c900a246a0329f52d914adc978d847a453324bbaa65afeebb490c55","observation_id":"99146d75-9310-4b65-b881-44323d1dc1e5","resolution":{"observed_at":"2026-08-06T17:56:03.792775Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.773821Z","title":"Self- Prompting Large Vision Models for Few-Shot Med- ical Image Segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.773821Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:49dc5858719d859d0f6eedb898aad9ba2dcdf23cbdc7e99baeae59b6782e84ee","observation_id":"e3d198ab-0394-424d-866f-fd67eaa15ef9","resolution":{"observed_at":"2026-08-06T17:56:03.773821Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:05.341598Z","title":"Integrating multi-scale informa- tion and diverse prompts in large model SAM-Med2D for accurate left ventricular ejection fraction estima- tion","venue":null,"work_id":"021b5548-432c-4e7e-8070-758b4dd7a913","year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.776779Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:6ba293a1b305055cbf84d541e67a2530b14faa2d8d7141ed2c32c485234141e5","observation_id":"59e54dff-fab0-49c7-831b-a87772afc910","resolution":{"observed_at":"2026-08-06T17:56:05.346248Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.06692","last_updated":"2025-01-12T02:57:04Z","snapshot_observed_at":"2026-08-03T04:13:11.611905Z","submitted_at":"2025-01-12T02:57:04Z","title":"PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.06692","snapshot_observed_at":"2026-08-06T17:56:03.803147Z","title":"PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Im- age Segmentation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.803147Z"},"links":{"cited_paper":"/paper/2501.06692","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:de3d615c11aff7c85d17bcdf744952024541421d1f0922b7b26a198fd4bd834d","observation_id":"463fb685-a52d-4916-896a-d73e58d8ee0f","resolution":{"observed_at":"2026-08-06T17:56:03.803147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.05393","last_updated":"2024-12-28T09:34:11Z","snapshot_observed_at":"2026-07-06T19:12:23.792177Z","submitted_at":"2024-09-09T07:43:58Z","title":"TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation","version":2},"cited_work":{"arxiv_id":"2409.05393","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.05393","snapshot_observed_at":"2026-08-06T17:56:04.139475Z","title":"TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation","venue":"cs.CV","work_id":"33b2a392-8514-44af-ac26-a09cebcf373e","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.806855Z"},"links":{"cited_paper":"/paper/2409.05393","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:2ee72aa3e49cb3a289ad99785d4ab030e44f8d7dcdd5d1fb8c641b4b6e7e8776","observation_id":"90c93ea0-ca7c-4711-96fb-7d221a108d7c","resolution":{"observed_at":"2026-08-06T17:56:04.143341Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19917","last_updated":"2024-12-27T20:33:39Z","snapshot_observed_at":"2026-08-03T18:15:57.284297Z","submitted_at":"2024-12-27T20:33:39Z","title":"Char-SAM: Turning Segment Anything Model into Scene Text Segmentation Annotator with Character-level Visual Prompts","version":1},"cited_work":{"arxiv_id":"2412.19917","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.19917","snapshot_observed_at":"2026-08-06T17:56:04.204098Z","title":"Char-SAM: Turning Segment Anything Model into Scene Text Segmentation Annotator with Character-level Visual Prompts","venue":"cs.CV","work_id":"b81efe9f-33e2-432a-8b68-0b3f7ed8ec2a","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.785826Z"},"links":{"cited_paper":"/paper/2412.19917","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:ecf243f7f78ac1ea1ee214b72a37d4b92f322dd05bcf10dca6c575fe50dd57ed","observation_id":"ff199018-7170-401f-945e-7776b9363f61","resolution":{"observed_at":"2026-08-06T17:56:04.207661Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.17363","last_updated":"2024-11-26T12:12:12Z","snapshot_observed_at":"2026-07-06T19:57:14.289258Z","submitted_at":"2024-11-26T12:12:12Z","title":"SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.17363","snapshot_observed_at":"2026-08-06T17:56:03.789703Z","title":"SAM-MPA: Applying SAM to Few- shot Medical Image Segmentation using Mask Propa- gation and Auto-prompting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.789703Z"},"links":{"cited_paper":"/paper/2411.17363","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:363699fea4ee362052c797061d24b273aea258272349b78e675cd8765a2d484b","observation_id":"aac91243-ab07-4011-afe8-9673e62f9d9d","resolution":{"observed_at":"2026-08-06T17:56:03.789703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.08746","last_updated":"2023-12-21T11:56:08Z","snapshot_observed_at":"2026-07-06T16:07:06.809394Z","submitted_at":"2023-08-17T02:51:01Z","title":"SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation","version":2},"cited_work":{"arxiv_id":"2308.08746","doi":null,"metadata_source":"pith","pith_arxiv_id":"2308.08746","snapshot_observed_at":"2026-08-06T17:56:04.107820Z","title":"SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation","venue":"cs.CV","work_id":"e98a6e23-2223-4409-aecd-3af8e9235fc1","year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.817522Z"},"links":{"cited_paper":"/paper/2308.08746","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:cb50b589507bcd37c9a268cd56d342199e383b07afa9b309c690fbf3ab7fa35e","observation_id":"24b2c8de-2640-4836-b7e8-210d704f6dc6","resolution":{"observed_at":"2026-08-06T17:56:04.111852Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.12231","last_updated":"2023-08-23T16:13:58Z","snapshot_observed_at":"2026-07-06T16:09:35.920101Z","submitted_at":"2023-08-23T16:13:58Z","title":"SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation","version":1},"cited_work":{"arxiv_id":"2308.12231","doi":null,"metadata_source":"pith","pith_arxiv_id":"2308.12231","snapshot_observed_at":"2026-08-06T17:56:04.172574Z","title":"SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation","venue":"eess.IV","work_id":"b8eae659-aa3b-435b-996b-7fcbbe3ffc83","year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.796432Z"},"links":{"cited_paper":"/paper/2308.12231","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:a0f825a93313f72e514b89bc0c99f306a069b5ff12d03a686aa2041feb6699ae","observation_id":"4fcfca8c-a9ab-4f51-88dc-dc4c55165568","resolution":{"observed_at":"2026-08-06T17:56:04.176326Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.12484","last_updated":"2022-10-13T01:53:23Z","snapshot_observed_at":"2026-07-06T13:04:02.391482Z","submitted_at":"2022-04-26T17:55:04Z","title":"ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.12484","snapshot_observed_at":"2026-08-06T17:56:03.799771Z","title":"ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.799771Z"},"links":{"cited_paper":"/paper/2204.12484","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:8f4f96a881a96ec86e8891c74adf8c287e92dca31473dc474910fc38ec471c9e","observation_id":"c43331d7-32b4-49a9-b036-c96b36276fa0","resolution":{"observed_at":"2026-08-06T17:56:03.799771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18858","last_updated":"2024-11-28T01:58:28Z","snapshot_observed_at":"2026-07-06T19:58:18.076549Z","submitted_at":"2024-11-28T01:58:28Z","title":"COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detection","version":1},"cited_work":{"arxiv_id":"2411.18858","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.18858","snapshot_observed_at":"2026-08-06T17:56:04.082815Z","title":"COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detection","venue":"cs.CV","work_id":"202618a9-ed1b-4dd9-b32b-d95ad0d320c3","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.827892Z"},"links":{"cited_paper":"/paper/2411.18858","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:0e8e8d871d992f11e498a297e58a76f9eb539fa4958c1a580dad1b04f0027b32","observation_id":"11ecc90a-50e5-4d6a-9ddb-360ec1187ab4","resolution":{"observed_at":"2026-08-06T17:56:04.087049Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08083","last_updated":"2024-02-01T08:05:26Z","snapshot_observed_at":"2026-08-07T22:37:17.297156Z","submitted_at":"2024-01-16T03:21:42Z","title":"UV-SAM: Adapting Segment Anything Model for Urban Village Identification","version":2},"cited_work":{"arxiv_id":"2401.08083","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.08083","snapshot_observed_at":"2026-08-06T17:56:04.067302Z","title":"UV-SAM: Adapting Segment Anything Model for Urban Village Identification","venue":"cs.CV","work_id":"d5f6e0c5-cf8c-4f45-b667-ce429ad512b2","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.831589Z"},"links":{"cited_paper":"/paper/2401.08083","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:3eba2a8e7b7b79aab82b5e34fd4f484e7b06386a5845bdc362591a40717873af","observation_id":"0eed46f3-8f41-4750-bb05-bdc70fcb0d67","resolution":{"observed_at":"2026-08-06T17:56:04.072001Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-031-72890-7_24","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T00:03:56.115653Z","title":"Pro2SAM: Mask Prompt to SAM with Grid Points for Weakly Supervised Object Localization","venue":"Lecture notes in computer science","work_id":"35ca4fd3-0021-4b82-ab99-b53c09979445","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.810802Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:61bddb2bbf251e0bf031ab5114c05ba6894896c824df29cc856e4911596c8fa1","observation_id":"bd5da4fd-8bf4-4d08-b59e-0d8c6d0d5889","resolution":{"observed_at":"2026-08-06T17:56:03.894626Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.14481","last_updated":"2024-03-23T03:13:35Z","snapshot_observed_at":"2026-07-06T17:06:58.615343Z","submitted_at":"2023-12-22T07:17:51Z","title":"SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation","version":2},"cited_work":{"arxiv_id":"2312.14481","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.14481","snapshot_observed_at":"2026-08-06T17:56:04.123312Z","title":"SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation","venue":"cs.CV","work_id":"cef1e8fe-c4ab-4996-9914-414d7f4bf44f","year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.813856Z"},"links":{"cited_paper":"/paper/2312.14481","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:b71d41c9e598f21c658174f8aa7cc46b6586f3b46d05c20b97b64f1d6e8518b8","observation_id":"10715ede-db99-4703-8881-a371319c2727","resolution":{"observed_at":"2026-08-06T17:56:04.127348Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:05.302167Z","title":"Automatic Seg- mentation Annotation of Space Target Using Segment Anything Model and Object Detection Prompts","venue":null,"work_id":"18e96ba5-67ba-4bcf-99fc-6b115024b99a","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.841643Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:ee7391a037c75d8459ffb0c3f4e45343dd4266e8a1f566e17b9b711c17775dd5","observation_id":"d67ca4de-c1c1-4530-92be-91df47bfe18d","resolution":{"observed_at":"2026-08-06T17:56:05.306908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:05.314923Z","title":"A Survey on Segment Any- thing Model (SAM): Vision Foundation Model Meets Prompt Engineering","venue":null,"work_id":"ef31509e-647a-4ff9-a7f0-8ce124a5ea43","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.821618Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:69b6291c24ca8db7a0decfb7474a8df05152c5edc5a69376dcc64347954fc578","observation_id":"6cbe4cec-e8fd-4ac2-b211-ee1154ca50d3","resolution":{"observed_at":"2026-08-06T17:56:05.319197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.08196","last_updated":"2023-05-19T16:33:03Z","snapshot_observed_at":"2026-08-05T18:19:34.804424Z","submitted_at":"2023-05-14T16:23:22Z","title":"A Comprehensive Survey on Segment Anything Model for Vision and Beyond","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.08196","snapshot_observed_at":"2026-08-06T17:56:03.824585Z","title":"A Comprehensive Survey on Seg- ment Anything Model for Vision and Beyond","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.824585Z"},"links":{"cited_paper":"/paper/2305.08196","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:868dca93d87a90654ed461a07dc326b4b42d5fd6b41fca07139f317ca3b8772e","observation_id":"7c4ee73d-5102-4752-ba2e-58d8b1209621","resolution":{"observed_at":"2026-08-06T17:56:03.824585Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.00695","last_updated":"2024-09-01T11:00:18Z","snapshot_observed_at":"2026-07-06T19:08:52.993904Z","submitted_at":"2024-09-01T11:00:18Z","title":"Curriculum Prompting Foundation Models for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":"2409.00695","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.00695","snapshot_observed_at":"2026-08-06T17:56:03.962381Z","title":"Curriculum Prompting Foundation Models for Medical Image Segmentation","venue":"cs.CV","work_id":"f47395d4-bee4-495d-916e-ee9b2601c590","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.851147Z"},"links":{"cited_paper":"/paper/2409.00695","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:5ca0fc69f802fb80eb83ec83668949fbc6890ed9dea1fb368929017b65cfab9e","observation_id":"477c555b-863c-4d69-89ef-d81d3f2f58dd","resolution":{"observed_at":"2026-08-06T17:56:03.966270Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.06660","last_updated":"2025-09-07T19:09:41Z","snapshot_observed_at":"2026-07-06T17:00:00.321552Z","submitted_at":"2023-12-11T18:59:52Z","title":"EdgeSAM: Prompt-In-the-Loop Distillation for SAM","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.06660","snapshot_observed_at":"2026-08-06T17:56:03.855214Z","title":"EdgeSAM: Prompt-In-the-Loop Distillation for On-Device Deployment of SAM","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.855214Z"},"links":{"cited_paper":"/paper/2312.06660","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:a63211022efb2b12c1b46cc6989ca547dc686e8aed08266c312468e3d6c8fc03","observation_id":"b448b334-fcd0-4656-93e6-de55560d783f","resolution":{"observed_at":"2026-08-06T17:56:03.855214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03678","last_updated":"2023-08-11T04:23:29Z","snapshot_observed_at":"2026-07-06T15:23:52.761222Z","submitted_at":"2023-05-05T16:48:45Z","title":"Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.03678","snapshot_observed_at":"2026-08-06T17:56:03.835409Z","title":"Towards Segment Any- thing Model (SAM) for Medical Image Segmentation: A Survey","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.835409Z"},"links":{"cited_paper":"/paper/2305.03678","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:579bc689ad2fc57e1f3834cd0565e3578f3c135f52369d70d423ca341d489be6","observation_id":"a5212793-1c11-4e3b-ae40-15e19fe40275","resolution":{"observed_at":"2026-08-06T17:56:03.835409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.10529","last_updated":"2024-03-18T08:47:03Z","snapshot_observed_at":"2026-07-06T16:49:04.651019Z","submitted_at":"2023-11-17T13:49:00Z","title":"Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.10529","snapshot_observed_at":"2026-08-06T17:56:03.838493Z","title":"Enhancing the Reliability of Seg- ment Anything Model for Auto-Prompting Medical Im- age Segmentation with Uncertainty Rectification","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.838493Z"},"links":{"cited_paper":"/paper/2311.10529","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:045a5dbeda0d3c590c855bc7fa6e796d74b49482605f9c7cfd5d8b4acb727bc8","observation_id":"f88cf439-37fe-4eda-9b9b-7d550bb4b553","resolution":{"observed_at":"2026-08-06T17:56:03.838493Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.06718","last_updated":"2023-07-11T18:13:14Z","snapshot_observed_at":"2026-08-05T05:31:21.900215Z","submitted_at":"2023-04-13T17:59:40Z","title":"Segment Everything Everywhere All at Once","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.06718","snapshot_observed_at":"2026-08-06T17:56:03.866876Z","title":"Segment Everything Everywhere All at Once","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.866876Z"},"links":{"cited_paper":"/paper/2304.06718","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:2ad8a6fbf0668715ce667c30cf602386691a40e3a2ac4660929cf1bb8b141286","observation_id":"8e008236-00e6-4e64-8d6f-cc78ffb88613","resolution":{"observed_at":"2026-08-06T17:56:03.866876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.845024Z","title":"Semantic-Enhanced Point-Box Joint Prompting for Video Object Segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.845024Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:b086e02383c72a6984e11d8c200031e71ccad560955318cd3fbb76b76c17a40c","observation_id":"8d361070-cdfa-4a80-9a83-c437165454c3","resolution":{"observed_at":"2026-08-06T17:56:03.845024Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.12156","last_updated":"2023-06-21T10:08:29Z","snapshot_observed_at":"2026-07-06T15:45:01.961896Z","submitted_at":"2023-06-21T10:08:29Z","title":"Fast Segment Anything","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.12156","snapshot_observed_at":"2026-08-06T17:56:03.848050Z","title":"Fast Segment Anything","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.848050Z"},"links":{"cited_paper":"/paper/2306.12156","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:2fe13b120ab878c61f706804f9405353a0e090dae476da67c587c9c9fa55141f","observation_id":"97323e76-0230-4f2c-9d8b-69ce71a072c3","resolution":{"observed_at":"2026-08-06T17:56:03.848050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.00924","last_updated":"2024-09-02T03:40:07Z","snapshot_observed_at":"2026-08-07T18:20:42.834568Z","submitted_at":"2024-09-02T03:40:07Z","title":"MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM","version":1},"cited_work":{"arxiv_id":"2409.00924","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.00924","snapshot_observed_at":"2026-08-06T17:56:03.936985Z","title":"MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM","venue":"cs.CV","work_id":"922d5600-3883-40d1-9bd4-c98c7065e59c","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.859273Z"},"links":{"cited_paper":"/paper/2409.00924","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:a5a1f26402b87d54b754de1a0716720b73ee6160290096db36a5aedd936be324","observation_id":"720937dc-a082-4fbe-bf8e-34b49107847b","resolution":{"observed_at":"2026-08-06T17:56:03.941307Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.06594","last_updated":"2023-03-12T07:22:08Z","snapshot_observed_at":"2026-07-06T15:01:49.803305Z","submitted_at":"2023-03-12T07:22:08Z","title":"ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.06594","snapshot_observed_at":"2026-08-06T17:56:03.863141Z","title":"ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual De- scriptions","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.863141Z"},"links":{"cited_paper":"/paper/2303.06594","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:624a4e467fa6843e7ea5ed7e201cd354e3dafe27dc71de0824a7f544d28778a4","observation_id":"693d6196-8f77-4f8b-8a6c-a0382fd50ecd","resolution":{"observed_at":"2026-08-06T17:56:03.863141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.17933","last_updated":"2024-07-25T10:46:29Z","snapshot_observed_at":"2026-07-06T18:51:45.603762Z","submitted_at":"2024-07-25T10:46:29Z","title":"Segmentation by registration-enabled SAM prompt engineering using five reference images","version":1},"cited_work":{"arxiv_id":"2407.17933","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.17933","snapshot_observed_at":"2026-08-06T17:56:05.123569Z","title":"Segmentation by registration-enabled SAM prompt engineering using five reference images","venue":"cs.CV","work_id":"cedb8886-1f7e-4219-a09e-16bf55fb8f5b","year":2024},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.554013Z"},"links":{"cited_paper":"/paper/2407.17933","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:caaf7e0ec4adfd220ae75d2748a6461dc40a1cf7f968a5b32ddb2a5c56ff0c85","observation_id":"585da220-c1a7-46dd-8828-5b48ff51dcc4","resolution":{"observed_at":"2026-08-06T17:56:05.127452Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00630","last_updated":"2025-02-02T02:42:24Z","snapshot_observed_at":"2026-08-06T04:38:05.975555Z","submitted_at":"2025-02-02T02:42:24Z","title":"Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation","version":1},"cited_work":{"arxiv_id":"2502.00630","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.00630","snapshot_observed_at":"2026-08-06T17:56:04.218088Z","title":"Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation","venue":"cs.CV","work_id":"5935147e-7033-4030-bd67-7eda24df9bd7","year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.782683Z"},"links":{"cited_paper":"/paper/2502.00630","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:1ac884303070515fc00657bc059b4a5e343b30ab4a5129827524fe188bf6dd74","observation_id":"0358123a-c8d1-4b95-8da5-fcf19de2880d","resolution":{"observed_at":"2026-08-06T17:56:04.221739Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:56:03.632855Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":3580,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.632855Z"},"links":{"citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:320585a6eb7c8f3384706a09a22a80d31e50991b06fd96f13e07e324d4861bc5","observation_id":"c285a3da-ee4f-4e08-8881-3b16ecc97c33","resolution":{"observed_at":"2026-08-06T17:56:03.632855Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T07:43:08.924247Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges"},"reference_resolution":{"displayed":95,"state_counts":{"malformed_identifier":3,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":48,"verified_exact":40,"verified_fuzzy":3},"total_outbound_references":95},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 95 of 95 outbound references and 0 inbound Pith citation observations for arXiv:2507.09562."}