{"as_of":"2026-08-07T23:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3e769bef46dbacb80b1529aaf189133c020f7d18377ed0c03c693cf97026ca3d","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:59:33.655694Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T07:40:17.970860Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.03706","snapshot_observed_at":"2026-08-01T07:40:17.970860Z","title":"arXiv preprint arXiv:2506.03706 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21371","last_updated":"2026-07-23T14:37:27Z","snapshot_observed_at":"2026-08-07T17:38:11.183534Z","submitted_at":"2026-07-23T14:37:27Z","title":"DINOde: Continuous Vision-Text Alignment for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T07:40:17.970860Z"},"links":{"cited_paper":"/paper/2506.03706","citing_paper":"/paper/2607.21371"},"observation_digest":"sha256:1b02a00a1f638dafb2b05833d0061dd6ae4bfbba4b88bb81cf33667b86aa5a30","observation_id":"61efe6ec-ec09-4db4-872a-3e2e77fbd26f","resolution":{"observed_at":"2026-08-01T07:40:17.970860Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.03706/citation-record","integrity":"/paper/2506.03706/integrity","json":"/paper/2506.03706/citation-record.json","paper":"/paper/2506.03706"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:59:34.081358Z","title":"What a mess: Multi-domain evaluation of zero-shot semantic segmentation","venue":null,"work_id":"cbaed690-6ad9-4725-8dfa-8343c9a8a4fe","year":2024},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:32.728367Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:3a261ea049cdff087e192f7d5cd6bf35b5e257dec38daee35cac252b6a9cff7c","observation_id":"1f83fc3a-9bce-472c-96d6-3527f31fdeaf","resolution":{"observed_at":"2026-08-07T10:59:34.084870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:34.069023Z","title":"Coco- stuff: Thing and stuff classes in context","venue":null,"work_id":"9e406e2f-9048-4155-8737-42edc47aa749","year":2018},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:32.781105Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:218b6c8fc703ed8b3bebc161bdc7fe428e3b496289cac361b08a7dee6a74e8ff","observation_id":"cdb955c7-dcf4-487a-a415-89a08e824bd9","resolution":{"observed_at":"2026-08-07T10:59:34.073587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.01253","last_updated":"2023-02-09T23:26:37Z","snapshot_observed_at":"2026-07-06T13:59:15.998573Z","submitted_at":"2022-10-03T22:21:07Z","title":"PLOT: Prompt Learning with Optimal Transport for Vision-Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.01253","snapshot_observed_at":"2026-08-07T10:59:32.858957Z","title":"Plot: Prompt learning with optimal transport for vision-language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:32.858957Z"},"links":{"cited_paper":"/paper/2210.01253","citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:6b8d9597a0ef0961785980503f016632aab2ebf8aa2c70babb119d3ce9b94c7b","observation_id":"a76a15da-a26a-4dae-8c3b-0bf782d138b0","resolution":{"observed_at":"2026-08-07T10:59:32.858957Z","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":"2026-08-07T10:59:34.054589Z","title":"Cat- seg: Cost aggregation for open-vocabulary semantic seg- mentation","venue":null,"work_id":"cdb255e8-1ecd-4ed1-93da-6d2209d255f2","year":2024},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:32.951599Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:202e7b111218c555293522249743b987019a6bdba1e808ef92afdddb4420674c","observation_id":"397d846c-e2cd-4c34-b6d2-2ec7a662ab97","resolution":{"observed_at":"2026-08-07T10:59:34.059588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:34.041991Z","title":"Sinkhorn distances: Lightspeed computation of optimal transport","venue":null,"work_id":"4fef361a-3792-443c-aded-f6b54a4cdeff","year":2013},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.029450Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:bf33e64901d9f32f22b041d834422f181bf6e789b67c7c7967bee50705513c0d","observation_id":"0b01ce4d-99e6-41e6-b78a-a69310e20e10","resolution":{"observed_at":"2026-08-07T10:59:34.046472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:34.030207Z","title":"De- coupling zero-shot semantic segmentation","venue":null,"work_id":"66d0d14e-3d01-4ea5-bc97-c4014296555d","year":2022},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.119657Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:ac802631ce1d60a1fce609025ecc692aae40660cbda491e9185646eecc016919","observation_id":"4f93f6e2-3d13-4ad1-bd8e-92c8fdaf801f","resolution":{"observed_at":"2026-08-07T10:59:34.033825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:34.018009Z","title":"Open- vocabulary panoptic segmentation maskclip","venue":null,"work_id":"3cf5f854-8b76-4710-b851-7cad4b00f695","year":2022},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.124442Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:94049389eedf1621f54782d9b77e04de1d95cbd95ccbe6058895eaf6c186a650","observation_id":"808ec51e-f675-48cb-91e9-7f562f7fdba3","resolution":{"observed_at":"2026-08-07T10:59:34.021983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:34.006507Z","title":"The pascal visual object classes challenge: A retrospective","venue":null,"work_id":"a32a7101-9646-478b-8454-c5421d2d92eb","year":2015},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.149823Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:1149eed51fd8f3cf247cddb24190829b1ecd3645bac41405fbfb8ad5a3588778","observation_id":"13cfe672-186e-4ac3-aea9-65bc5f74f6fd","resolution":{"observed_at":"2026-08-07T10:59:34.010134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.995636Z","title":"Scal- ing open-vocabulary image segmentation with image-level labels","venue":null,"work_id":"c45e1dac-2b4f-4f75-8e98-f56f5b6d7ae2","year":2022},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.259762Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:36f0305603ae8bef36bef5a627385cdaf5859b5f242caaee6a6879b0e8c9ff82","observation_id":"c9ad73b5-21d2-46f7-b609-eab118b48998","resolution":{"observed_at":"2026-08-07T10:59:33.999680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.364157Z","title":"Scaling up visual and vision-language representa- tion learning with noisy text supervision","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.364157Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:5034893be384d85068479c5801b68014467341ddc635de9071b4d1ba9958b13c","observation_id":"cf8f142a-5204-4064-b167-986418ed7def","resolution":{"observed_at":"2026-08-07T10:59:33.364157Z","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":"2026-08-07T10:59:33.978327Z","title":"End-to-end learning of geometry and context for deep stereo regression","venue":null,"work_id":"89feca34-a7bb-4ad8-9cb4-52736dee73bf","year":2017},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.449603Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:6abe60e8bca87bf7105eb4bb95052b0a09f9a5a360a7baaef009e03e1bad1d3f","observation_id":"943e30a8-9885-4506-85ee-7e9091f49ad5","resolution":{"observed_at":"2026-08-07T10:59:33.982064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.12171","last_updated":"2023-05-30T13:46:57Z","snapshot_observed_at":"2026-07-06T14:45:33.278327Z","submitted_at":"2023-01-28T11:51:20Z","title":"ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts","version":2},"cited_work":{"arxiv_id":"2301.12171","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.12171","snapshot_observed_at":"2026-08-07T10:59:33.726800Z","title":"ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts","venue":"cs.CV","work_id":"d2d10ff2-0d7f-4bee-9165-f39ce3146387","year":2023},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.538544Z"},"links":{"cited_paper":"/paper/2301.12171","citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:9ccb01192504f3b27b96c61b0d22c7d63b5f8555755871d4ca495df845c5adc3","observation_id":"0089f831-3924-46f0-9c56-82e7f0e13bda","resolution":{"observed_at":"2026-08-07T10:59:33.731261Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.10054","last_updated":"2022-02-21T09:03:34Z","snapshot_observed_at":"2026-08-04T02:30:25.691953Z","submitted_at":"2022-02-21T09:03:34Z","title":"Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.10054","snapshot_observed_at":"2026-08-07T10:59:33.573137Z","title":"Fine-tuning can distort pretrained fea- tures and underperform out-of-distribution","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.573137Z"},"links":{"cited_paper":"/paper/2202.10054","citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:7fb0bdb0ae87a9016de17c3468224541264ee382f69bbea2203f8d979461958e","observation_id":"8e496662-8890-4da8-abdb-b9f7de4c6680","resolution":{"observed_at":"2026-08-07T10:59:33.573137Z","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":"2026-08-07T10:59:33.577196Z","title":"Open-vocabulary semantic segmentation with mask-adapted clip","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.577196Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:f130581219ba7fb1e4b39774ff56500b4504ad8b421f969eb80f0e4e4ec39916","observation_id":"f86e5f25-a76d-41ca-9fde-141b54318f5d","resolution":{"observed_at":"2026-08-07T10:59:33.577196Z","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":"2026-08-07T10:59:33.959508Z","title":"Graftnet: Towards domain generalized stereo matching with a broad-spectrum and task-oriented feature","venue":null,"work_id":"abcbbd38-11b7-427e-8444-133b5539eef0","year":2022},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.580954Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:051e68943b40aed360db3d7f664c5f7b44a85ffb85c6633507d1caa34c3199c9","observation_id":"7c4aba21-ff10-43db-b387-c7fc0c0a074c","resolution":{"observed_at":"2026-08-07T10:59:33.963846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.948331Z","title":"M ´emoire sur la th ´eorie des d ´eblais et des remblais","venue":null,"work_id":"58e07a1a-241b-4cef-87b7-417996b14f34","year":null},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.584386Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:349a196353abacdb76e490cbef273c78fb08cf6c5ee37d1a1ff1620dc49dbd3f","observation_id":"424fed2d-5b97-4c35-bcfb-9acb911d3047","resolution":{"observed_at":"2026-08-07T10:59:33.952261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.936259Z","title":"Computational optimal transport: With applications to data science","venue":null,"work_id":"25657a86-a3a5-49b8-9adc-73b94d5dbadc","year":2019},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.587860Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:a8ac002bb8488ccf2340a9ec6fa32ab5de7ad15ce5ddef55a6c31350720e56ae","observation_id":"ce54dcc1-c8b7-4369-b819-dcfca594657e","resolution":{"observed_at":"2026-08-07T10:59:33.940608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.591334Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.591334Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:a4b022ac112e1cad4e7bdbb0f539d995f7677313d6172495b89de9ace225c4f1","observation_id":"06591543-8142-4a1b-a6b9-f96b3d012ece","resolution":{"observed_at":"2026-08-07T10:59:33.591334Z","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":"2026-08-07T10:59:33.917878Z","title":"Convo- lutional neural network architecture for geometric matching","venue":null,"work_id":"b2fb30ae-164f-4058-b794-d99f10b37793","year":2017},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.595168Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:112f2707d3c9f4855037cc8c80f20abbcbaa7d79cccb2e50d02f81637778edf0","observation_id":"37b73247-1906-4541-a8c6-bd863fef8d27","resolution":{"observed_at":"2026-08-07T10:59:33.921731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.16067","last_updated":"2024-07-22T21:54:19Z","snapshot_observed_at":"2026-07-06T18:50:22.540341Z","submitted_at":"2024-07-22T21:54:19Z","title":"LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies","version":1},"cited_work":{"arxiv_id":"2407.16067","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.16067","snapshot_observed_at":"2026-08-07T10:59:33.698019Z","title":"LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies","venue":"cs.LG","work_id":"98ee1e70-81aa-49b7-bb47-cd3e81ca5999","year":2024},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.598988Z"},"links":{"cited_paper":"/paper/2407.16067","citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:2774c70e852e9ff2e66b3a103600df2c49f152e6b4c662eaabba0b80ba4efe6d","observation_id":"afd3afe9-cd87-4de7-bd90-168b6c43f043","resolution":{"observed_at":"2026-08-07T10:59:33.704101Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.907203Z","title":"Fully complex-valued fully con- volutional multi-feature fusion network (fc 2 mfn) for build- ing segmentation of insar images","venue":null,"work_id":"973f7b87-52e7-4fae-8564-d73e9d83106d","year":2022},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.603005Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:981d0d22776b1fcd8e9c46678d56c07566aaedd308532f373db6c73ffa3e295b","observation_id":"fa52c753-da62-4f1b-aa2f-f1c673ce1d39","resolution":{"observed_at":"2026-08-07T10:59:33.911215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.896152Z","title":"Deepmao: Deep multi-scale aware over- complete network for building segmentation in satellite im- agery","venue":null,"work_id":"6641cf11-6ae2-40ed-9862-3b62e4308b61","year":2023},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.606227Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:c991dfe33b1d937dc7869f3bd4e105699fb3a39a47fa2e129382f96a611d9f4a","observation_id":"bd6afbc5-3a45-4045-8553-7824465c13f1","resolution":{"observed_at":"2026-08-07T10:59:33.899727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.884217Z","title":"Fully complex-valued deep learning model for visual percep- tion","venue":null,"work_id":"84606cc9-58f1-4903-96ab-c87b710f9822","year":2023},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.609030Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:24eaa90cf33969324dd895e7ccc22e6a0db0a17b6dac0c98326bb0ef9e09e503","observation_id":"dfa4146c-cc45-4abf-bc35-ae4fc44c6fab","resolution":{"observed_at":"2026-08-07T10:59:33.888775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.871703Z","title":"Ogp- net: Optical guidance meets pixel-level contrastive distilla- tion for robust multi-modal and missing modality segmen- tation","venue":null,"work_id":"d375b097-8e98-4cd2-a5b1-3d4db880f5ed","year":2025},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.612617Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:f91a157f87bcea272603a7fb70d6a488ee52d3557a2503c26fea7da8dd7f406a","observation_id":"ccb27fe0-4ce3-4fb6-bcac-831d288f7046","resolution":{"observed_at":"2026-08-07T10:59:33.877334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.859976Z","title":"Mrfp: Learning generalizable semantic segmentation from sim-2-real with multi-resolution feature perturbation","venue":null,"work_id":"ba4f915c-5d89-4560-9d2b-250fc0a1d216","year":2024},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.616135Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:023097049e79abe3b579a340ad6154b332ce3537a27b088bec2e7fc21bedff95","observation_id":"2cfa2d56-b566-4e44-a5a5-da2e98162e9c","resolution":{"observed_at":"2026-08-07T10:59:33.864797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.848003Z","title":"Robust fine-tuning of zero-shot models","venue":null,"work_id":"4604d9bf-b5bf-4dd2-b7a6-452dfb25d0bb","year":2022},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.619200Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:70288dd29800e164a2a1b6a5cef6df18e8cb37799ec081b82ad481e98c171b7a","observation_id":"8a00ff46-d0a2-43c4-bbf1-6ba658529f0b","resolution":{"observed_at":"2026-08-07T10:59:33.852682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.622467Z","title":"Open-vocabulary panop- tic segmentation with text-to-image diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.622467Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:e451b855f9a685135c277dc1d7842213a9c2ea3dc1425aa0ddca08085137ead6","observation_id":"f9c4855e-3679-48ad-83e7-b2f5457e3812","resolution":{"observed_at":"2026-08-07T10:59:33.622467Z","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":"2026-08-07T10:59:33.829543Z","title":"A simple baseline for open- vocabulary semantic segmentation with pre-trained vision- language model","venue":null,"work_id":"87ed96eb-607f-4c4f-b8cf-cbb5a475fea8","year":2022},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.625491Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:1a742b78bd6f282846d808d43cbdd66813c5ed1eeb1fb4670c2cdbf164e12953","observation_id":"6dd1ce5d-2c8c-4e8f-a0a2-edb295ca5cc0","resolution":{"observed_at":"2026-08-07T10:59:33.834417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.817051Z","title":"Side adapter network for open-vocabulary semantic segmentation","venue":null,"work_id":"f3c92fe9-55ef-4727-888c-c1adc69e1088","year":2023},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.628802Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:cf02833fec48b213cfefe9a8d8a1c3b15d44f942e3979df764dd2afd8c41b24b","observation_id":"dacd34eb-a35f-4497-9f6a-d61b69d64fe4","resolution":{"observed_at":"2026-08-07T10:59:33.822134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.632568Z","title":"Convolutions die hard: Open-vocabulary seg- mentation with single frozen convolutional clip","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.632568Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:3a92d9e88721193c697f3502639a46f5f04ebeed37a97e15f462da299a70921e","observation_id":"7cbe7378-a34a-42ef-9ffb-38854934e073","resolution":{"observed_at":"2026-08-07T10:59:33.632568Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.11432","last_updated":"2021-11-22T18:59:55Z","snapshot_observed_at":"2026-07-06T12:11:02.119174Z","submitted_at":"2021-11-22T18:59:55Z","title":"Florence: A New Foundation Model for Computer Vision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.11432","snapshot_observed_at":"2026-08-07T10:59:33.636363Z","title":"Florence: A new foundation model for computer vision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.636363Z"},"links":{"cited_paper":"/paper/2111.11432","citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:a3cdfe1b944470827ef8ae4762375042a026e1c4abd38ba21544ac7870dc184f","observation_id":"b58f1569-d8fe-460a-997f-be4a1cd9c771","resolution":{"observed_at":"2026-08-07T10:59:33.636363Z","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":"2026-08-07T10:59:33.795941Z","title":"Dept: Decoupled prompt tuning","venue":null,"work_id":"6f24e069-6dac-4748-b5b4-85c3b5e16b51","year":2024},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.640496Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:519cdade7957aad576a942f8530786c924c1c7339a7f5e4ddc42a66a4209ecf5","observation_id":"7c7f3539-e02b-4835-9cf1-ab2b505c2102","resolution":{"observed_at":"2026-08-07T10:59:33.799813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.783493Z","title":"Semantic under- standing of scenes through the ade20k dataset","venue":null,"work_id":"7d80c431-0bf5-445a-a743-b19ddfd50062","year":2019},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.644131Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:085d20fd4638cbc8cd250ead1c88bcfc22d4e17eed4a99e5b74f7376b2f999d4","observation_id":"49b8c517-98f1-46bc-94a4-60161dd02f31","resolution":{"observed_at":"2026-08-07T10:59:33.787709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.770425Z","title":"Extract free dense labels from clip","venue":null,"work_id":"319719d4-1f68-4a5c-91e4-981f27e31ca7","year":2022},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.647687Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:e4fcbf30968c89fdf351bad87db8fe325a90fc2fbe773759a4a6594697c241fb","observation_id":"0c07ca5a-c2ea-47c4-86e6-da30d7d18418","resolution":{"observed_at":"2026-08-07T10:59:33.774932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:59:33.651537Z","title":"Learning to prompt for vision-language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.651537Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:b85d87a9f82990e9cc165a1d0ef3f783615c5aead4b34a14ccaa913943fe20c8","observation_id":"610f8044-f23b-4181-a881-2f9d2b42daaa","resolution":{"observed_at":"2026-08-07T10:59:33.651537Z","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":"2026-08-07T10:59:33.750503Z","title":"Zegclip: Towards adapting clip for zero-shot seman- tic segmentation","venue":null,"work_id":"0283b24c-7643-43a5-92bb-3d3f7bb87ac9","year":2023},"citing_paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:33.655694Z"},"links":{"citing_paper":"/paper/2506.03706"},"observation_digest":"sha256:3b7faae28659aef9fae9d7d0a6acfef7dc767b2f0e96cbff13d02120a736b121","observation_id":"a9f1003b-5697-44e4-a866-dc69ee4a8426","resolution":{"observed_at":"2026-08-07T10:59:33.754512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.03706","last_updated":"2025-06-04T08:36:56Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T10:54:42.907137Z","submitted_at":"2025-06-04T08:36:56Z","title":"OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":2,"verified_fuzzy":25},"total_outbound_references":36},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2506.03706."}