{"as_of":"2026-08-06T16:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7598907aaba5b5f48a15d0c22581aa5bffa54ea67d4dae6e893eddfa00e1c9c0","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":18,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:18:56.289550Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-07T21:34:09.311757Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-08-06T15:18:56.289550Z","title":"Matcher: Segment anything with one shot using all-purpose feature matching","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16337","last_updated":"2025-07-22T08:19:56Z","snapshot_observed_at":"2026-08-06T15:09:40.524374Z","submitted_at":"2025-07-22T08:19:56Z","title":"One Polyp Identifies All: One-Shot Polyp Segmentation with SAM via Cascaded Priors and Iterative Prompt Evolution","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T15:18:56.289550Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2507.16337"},"observation_digest":"sha256:dda8267b1ca17a15049a67dd236849379fe414fabd5f339354a246d8cd8f3158","observation_id":"f8ac10fd-a231-4e2b-87d5-c4c685a1b3b3","resolution":{"observed_at":"2026-08-06T15:18:56.289550Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":"2305.13310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-07T21:34:09.311757Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":"cs.CV","work_id":"3b513f43-6507-4193-a03c-16234dfedc9c","year":2023},"citing_paper":{"arxiv_id":"2603.07436","last_updated":"2026-04-13T23:58:13Z","snapshot_observed_at":"2026-07-31T17:52:00.157490Z","submitted_at":"2026-03-08T03:16:49Z","title":"RPG-SAM: Reliability-Weighted Prototypes and Geometric Adaptive Threshold Selection for Training-Free One-Shot Polyp Segmentation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-15T15:19:03.906153Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2603.07436"},"observation_digest":"sha256:bc22cdd8cd8cc23e327ad1d1038337a3fdb58e68bf2e369f21abac2b2eed7630","observation_id":"06341536-66a8-4205-8e93-d25e47bfd669","resolution":{"observed_at":"2026-05-15T15:20:08.179556Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":"2305.13310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-07T21:34:09.311757Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":"cs.CV","work_id":"3b513f43-6507-4193-a03c-16234dfedc9c","year":2023},"citing_paper":{"arxiv_id":"2605.04501","last_updated":"2026-05-06T05:10:09Z","snapshot_observed_at":"2026-07-06T23:17:13.770090Z","submitted_at":"2026-05-06T05:10:09Z","title":"Example-Based Object Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-08T18:03:10.666870Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2605.04501"},"observation_digest":"sha256:a4593c837198779f2c6cf537ac5fdeeec8063bf77c819134382cd05e32c8499b","observation_id":"9f0cdfb4-1c0f-4eb8-afb2-073de2d5d40f","resolution":{"observed_at":"2026-05-09T06:50:41.207241Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":"2305.13310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-07T21:34:09.311757Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":"cs.CV","work_id":"3b513f43-6507-4193-a03c-16234dfedc9c","year":2023},"citing_paper":{"arxiv_id":"2605.17630","last_updated":"2026-05-19T18:36:14Z","snapshot_observed_at":"2026-08-02T23:17:13.063897Z","submitted_at":"2026-05-17T19:51:32Z","title":"SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-20T13:51:35.769341Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2605.17630"},"observation_digest":"sha256:1d6ab8411e5cd3850b8e6741f9dc3759d9edbea2c62bb5e4608cc870a8b7c3f3","observation_id":"1d385d8a-f354-4233-ab2f-66a73847d2ae","resolution":{"observed_at":"2026-05-20T13:53:19.826805Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":"2305.13310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-07T21:34:09.311757Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":"cs.CV","work_id":"3b513f43-6507-4193-a03c-16234dfedc9c","year":2023},"citing_paper":{"arxiv_id":"2605.17630","last_updated":"2026-05-19T18:36:14Z","snapshot_observed_at":"2026-08-02T23:17:13.063897Z","submitted_at":"2026-05-17T19:51:32Z","title":"SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-21T07:43:28.627414Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2605.17630"},"observation_digest":"sha256:fba2ad880b2320045022104608a020843ab3a062e8f7febd0c348efccb4f9031","observation_id":"65e66ad0-a79e-4cfa-901c-f0fe34cc5986","resolution":{"observed_at":"2026-05-21T07:44:02.807494Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":"2305.13310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-07T21:34:09.311757Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":"cs.CV","work_id":"3b513f43-6507-4193-a03c-16234dfedc9c","year":2023},"citing_paper":{"arxiv_id":"2606.07953","last_updated":"2026-06-06T03:06:10Z","snapshot_observed_at":"2026-07-31T12:59:23.300619Z","submitted_at":"2026-06-06T03:06:10Z","title":"Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-27T20:07:49.478014Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2606.07953"},"observation_digest":"sha256:bcf09216f8f31bc39cd9329bc43da81c1ce9b8b995d9fc28b6550527977af87a","observation_id":"03d91bee-1469-4c2a-a3c4-0b57cad86331","resolution":{"observed_at":"2026-07-02T20:47:23.243409Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":"2305.13310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-07T21:34:09.311757Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":"cs.CV","work_id":"3b513f43-6507-4193-a03c-16234dfedc9c","year":2023},"citing_paper":{"arxiv_id":"2606.07965","last_updated":"2026-06-06T03:48:12Z","snapshot_observed_at":"2026-07-06T23:47:33.680573Z","submitted_at":"2026-06-06T03:48:12Z","title":"Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-06-27T20:03:13.515338Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2606.07965"},"observation_digest":"sha256:3af2e57292cbaeaceed83a2395d061cade00cf739423f7217d6862ec89ef48f8","observation_id":"1d24f145-2f14-4a5a-b955-164c355abfc7","resolution":{"observed_at":"2026-07-02T20:57:23.249975Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":"2305.13310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-07T21:34:09.311757Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":"cs.CV","work_id":"3b513f43-6507-4193-a03c-16234dfedc9c","year":2023},"citing_paper":{"arxiv_id":"2606.09670","last_updated":"2026-06-08T15:52:05Z","snapshot_observed_at":"2026-08-01T16:33:35.092832Z","submitted_at":"2026-06-08T15:52:05Z","title":"Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-27T17:23:13.494169Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2606.09670"},"observation_digest":"sha256:9f2286b2832e9053d3ccfc8a0526e943dea38284ee7d696e4c1eb2850406a915","observation_id":"a4b873cd-4898-4ec0-83e3-fcbe4178304b","resolution":{"observed_at":"2026-07-03T00:17:28.729038Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":"2305.13310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-07T21:34:09.311757Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":"cs.CV","work_id":"3b513f43-6507-4193-a03c-16234dfedc9c","year":2023},"citing_paper":{"arxiv_id":"2606.24297","last_updated":"2026-06-23T08:19:30Z","snapshot_observed_at":"2026-08-06T13:46:22.881906Z","submitted_at":"2026-06-23T08:19:30Z","title":"Training-free Cross-domain Few-shot Segmentation via Robust Semantic Representation and Matching","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T01:00:05.183396Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2606.24297"},"observation_digest":"sha256:554e7c8e443edfd95730af9d97cd39fbf6454fe52c70df64fb3de4111d54e12d","observation_id":"2976a6c0-2ad2-4d9d-b807-167b68bf268e","resolution":{"observed_at":"2026-07-04T16:09:56.413471Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":"2305.13310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-07T21:34:09.311757Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":"cs.CV","work_id":"3b513f43-6507-4193-a03c-16234dfedc9c","year":2023},"citing_paper":{"arxiv_id":"2606.26711","last_updated":"2026-06-30T06:03:55Z","snapshot_observed_at":"2026-07-07T00:01:01.437499Z","submitted_at":"2026-06-25T07:44:37Z","title":"Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T05:41:09.896915Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2606.26711"},"observation_digest":"sha256:e9fd459ffdeb0f63c47404e6e98a2214961222d7824fc48d81a9429edf0b2d05","observation_id":"46681f07-7b73-4224-9256-4e43cdb18e2f","resolution":{"observed_at":"2026-07-04T12:59:52.435391Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":"2305.13310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-07T21:34:09.311757Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":"cs.CV","work_id":"3b513f43-6507-4193-a03c-16234dfedc9c","year":2023},"citing_paper":{"arxiv_id":"2606.26711","last_updated":"2026-06-30T06:03:55Z","snapshot_observed_at":"2026-07-07T00:01:01.437499Z","submitted_at":"2026-06-25T07:44:37Z","title":"Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-01T06:35:35.956203Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2606.26711"},"observation_digest":"sha256:664a4e90298fff450c1dbf41d8f3f9c4a9aeecb6a745e1c6b4f5599eb2c81c71","observation_id":"5797632a-2a96-4c0b-8f44-f5b27b1d0f69","resolution":{"observed_at":"2026-07-01T09:25:41.398481Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":"2305.13310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-07T21:34:09.311757Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":"cs.CV","work_id":"3b513f43-6507-4193-a03c-16234dfedc9c","year":2023},"citing_paper":{"arxiv_id":"2606.28920","last_updated":"2026-06-27T13:50:39Z","snapshot_observed_at":"2026-08-04T12:39:49.378908Z","submitted_at":"2026-06-27T13:50:39Z","title":"ExACT: Exemplar-Driven Calibrated Refinement for Training-Free Visual Grounding in Remote Sensing Images","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-30T09:47:19.140153Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2606.28920"},"observation_digest":"sha256:e63b8567f4f88b7cd0841ef0451d6a7a6e3c33f0171ead500c25befbbb8870f1","observation_id":"fced147a-75bd-4395-a3a9-041a75785951","resolution":{"observed_at":"2026-06-30T09:54:35.201340Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":"2305.13310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-07T21:34:09.311757Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":"cs.CV","work_id":"3b513f43-6507-4193-a03c-16234dfedc9c","year":2023},"citing_paper":{"arxiv_id":"2606.31136","last_updated":"2026-06-30T05:06:06Z","snapshot_observed_at":"2026-08-02T09:05:38.806456Z","submitted_at":"2026-06-30T05:06:06Z","title":"FROST: Training-Free Few-Shot Segmentation with Frozen Features and Nonparametric Statistics","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-01T06:48:55.341060Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2606.31136"},"observation_digest":"sha256:cb3650fe32f0901145425dc2e7a75be2d4ded732a8469dc8879b5926ccda5cd3","observation_id":"6746a5a1-6faa-40ed-b6b3-0a5c7fb1ba88","resolution":{"observed_at":"2026-07-01T08:55:35.955239Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":"2305.13310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-07T21:34:09.311757Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":"cs.CV","work_id":"3b513f43-6507-4193-a03c-16234dfedc9c","year":2023},"citing_paper":{"arxiv_id":"2607.05253","last_updated":"2026-07-07T04:11:07Z","snapshot_observed_at":"2026-08-06T14:48:18.940940Z","submitted_at":"2026-07-06T16:00:17Z","title":"Repurposing CLIP to Localize at Pixel Level","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-07T21:24:38.081297Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2607.05253"},"observation_digest":"sha256:dc68365f5c0c6a03e7194d8ae1f86ad64c46eabb1ba28788e33d1189eab3e417","observation_id":"1f7e637d-b78e-4c98-96f5-ebca4dbdbcbd","resolution":{"observed_at":"2026-07-07T21:34:09.312983Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-11T07:30:42.555053Z","title":"Matcher: Segment anything with one shot using all-purpose feature matching,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.05253","last_updated":"2026-07-07T04:11:07Z","snapshot_observed_at":"2026-08-06T14:48:18.940940Z","submitted_at":"2026-07-06T16:00:17Z","title":"Repurposing CLIP to Localize at Pixel Level","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-11T07:30:42.555053Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2607.05253"},"observation_digest":"sha256:e87e07cb4326c4ed1a439e4d2b855fe3da7a25b0e1fdf7ac6f29def2afa04fda","observation_id":"5f5e2f1c-e0a4-4efe-8f2c-bdd43a75dd3d","resolution":{"observed_at":"2026-07-11T07:30:42.555053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-07-14T03:33:38.303340Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11732","last_updated":"2026-07-13T15:55:45Z","snapshot_observed_at":"2026-08-05T12:08:08.299951Z","submitted_at":"2026-07-13T15:55:45Z","title":"GFR-SAM: Training-Free Referring Camouflaged Object Segmentation via Cross-Image Prompting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-14T03:33:38.303340Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2607.11732"},"observation_digest":"sha256:b7972863d42bf68db842f74064a5a05db30167a7f093ac778cfad05d2faa03cf","observation_id":"d81e2493-665b-4c10-afc4-9795a57b2cd5","resolution":{"observed_at":"2026-07-14T03:33:38.303340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-08-01T17:19:30.547759Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.17693","last_updated":"2026-07-20T08:41:47Z","snapshot_observed_at":"2026-08-06T05:31:02.765313Z","submitted_at":"2026-07-20T08:41:47Z","title":"Memory-Supported Synergistic Adaptation for Training-Free Test-Time Medical Image Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T17:19:30.547759Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2607.17693"},"observation_digest":"sha256:fe87d11a9224a2d42336c2c42cf5981d04274e01673cbb4c51ab44562cc37fdf","observation_id":"21b2bdfb-645a-4b98-9870-efe56376e2c9","resolution":{"observed_at":"2026-08-01T17:19:30.547759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-08-01T02:09:22.765762Z","title":"arXiv preprint arXiv:2305.13310 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.25563","last_updated":"2026-07-28T10:50:24Z","snapshot_observed_at":"2026-08-04T18:10:07.124662Z","submitted_at":"2026-07-28T10:50:24Z","title":"Few-Shot Open-Vocabulary Remote Sensing Segmentation via Textual Inversion","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T02:09:22.765762Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2607.25563"},"observation_digest":"sha256:9ce9f96a249fa437b78eba4fe27880ae03b1e81eecf1feb08f1868121f030c5a","observation_id":"7a7b969a-510c-4b61-8924-5be0fe81e25e","resolution":{"observed_at":"2026-08-01T02:09:22.765762Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2305.13310/citation-record","integrity":"/paper/2305.13310/integrity","json":"/paper/2305.13310/citation-record.json","paper":"/paper/2305.13310"},"outbound":[],"paper":{"arxiv_id":"2305.13310","last_updated":"2024-01-19T13:03:04Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T10:34:17.533932Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 inbound Pith citation observations for arXiv:2305.13310."}