{"as_of":"2026-08-10T07:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a7887c4e04984c7eff9d60cfd3b8d7e41301cf824f0d8a56f08fea1f56fe0cef","coverage":[{"denominator":79,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":79,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T14:33:26.893881Z","state":"measured"},{"denominator":79,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":79,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2508.21222/citation-record","integrity":"/paper/2508.21222/integrity","json":"/paper/2508.21222/citation-record.json","paper":"/paper/2508.21222"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:33:37.229577Z","title":"Flamingo: a visual language model for few-shot learning","venue":null,"work_id":"03d4d12a-27a8-4cd0-a0fe-0f239c9920f3","year":2022},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:20.407493Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:82176a3ddeff430538e1abc85c61deca60f81f2e9921196601df231f20ced0c9","observation_id":"030809c6-7ec4-4f40-97eb-b77dd96b5aea","resolution":{"observed_at":"2026-08-05T14:33:37.273057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.05814","last_updated":"2022-10-15T21:18:49Z","snapshot_observed_at":"2026-08-07T13:01:28.667448Z","submitted_at":"2021-12-10T20:15:03Z","title":"Deep ViT Features as Dense Visual Descriptors","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.05814","snapshot_observed_at":"2026-08-05T14:33:20.462407Z","title":"Deep vit features as dense visual descriptors","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:20.462407Z"},"links":{"cited_paper":"/paper/2112.05814","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:1e84e28bc73b3bd090071865235868236bb198adc6e8d9574dfe1e9b45f41b62","observation_id":"f49e918f-35f5-4eb9-996b-99abc183dc10","resolution":{"observed_at":"2026-08-05T14:33:20.462407Z","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":"2026-08-05T14:33:37.047570Z","title":"Unicom: Uni- versal and compact representation learning for image retrieval","venue":null,"work_id":"fc4eff22-d9c6-404c-bef9-6b400e15c592","year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:20.575033Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:cf1aaabf9debefcae8195f37e1b7c6757b4d8718144e79797dedf92ddb0d6a01","observation_id":"fd83821f-6620-4f22-a16a-15b33e6bc8da","resolution":{"observed_at":"2026-08-05T14:33:37.129834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:36.930102Z","title":"Self-supervised learning from images with a joint-embedding predictive architecture","venue":null,"work_id":"0583e63a-8576-4f1a-a07d-9845fc9ba61a","year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:20.659110Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:b3002e1356c528b44a31df61ac5a287888b2433a63f7ddecb1282dff0047b460","observation_id":"1085a783-1e95-431e-a42e-7691b8a26e5c","resolution":{"observed_at":"2026-08-05T14:33:37.006881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.01390","last_updated":"2023-08-07T17:53:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-08-02T19:10:23Z","title":"OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.01390","snapshot_observed_at":"2026-08-05T14:33:20.756225Z","title":"Openflamingo: An open- source framework for training large autoregressive vision- language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:20.756225Z"},"links":{"cited_paper":"/paper/2308.01390","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:1cffe96dafe98ce177571afadfe4cf0ac95fbf1a5e8b876b3fd2e644a79c6300","observation_id":"9b93ac58-f6e0-4e1b-9b39-2ca06270b4ae","resolution":{"observed_at":"2026-08-05T14:33:20.756225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13653","last_updated":"2023-05-23T03:53:57Z","snapshot_observed_at":"2026-08-08T15:42:31.538210Z","submitted_at":"2023-05-23T03:53:57Z","title":"RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13653","snapshot_observed_at":"2026-08-05T14:33:20.875913Z","title":"Rasa: Relation and sensitivity aware representation learning for text-based person search","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:20.875913Z"},"links":{"cited_paper":"/paper/2305.13653","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:8dc0d914c7b55a8cd69808f8ffd09c378b8e7e692a594f0762655bc09c88d87f","observation_id":"81767c0e-7b78-42d6-b72b-a1ba4c5fffcb","resolution":{"observed_at":"2026-08-05T14:33:20.875913Z","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":"2026-08-05T14:33:36.703449Z","title":"Learning local feature descriptors with triplets and shallow convolutional neural networks","venue":null,"work_id":"d6a6289a-a69a-4f9f-8ca4-4d4c27652978","year":2016},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:20.937372Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:95f45ffbd4f64ec678db00448500d6bd3b5f4929e19ee1a9ff1d62698ff99078","observation_id":"499ba28b-c85d-48b9-b0fc-e1fb5a7def8d","resolution":{"observed_at":"2026-08-05T14:33:36.786841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.08254","last_updated":"2022-09-03T14:11:33Z","snapshot_observed_at":"2026-07-06T11:19:34.705520Z","submitted_at":"2021-06-15T16:02:37Z","title":"BEiT: BERT Pre-Training of Image Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.08254","snapshot_observed_at":"2026-08-05T14:33:21.016121Z","title":"Beit: Bert pre-training of image transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:21.016121Z"},"links":{"cited_paper":"/paper/2106.08254","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:c72aa6f959133d4af110277157fad5fff36cc78a4988c0ca95ee2546aa467433","observation_id":"468526de-baee-4154-823e-dbf15fdbb506","resolution":{"observed_at":"2026-08-05T14:33:21.016121Z","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":"2026-08-05T14:33:21.077902Z","title":"Unsupervised learning of visual features by contrasting cluster assignments","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:21.077902Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:7f3d9b0129ed5faaa0547e0184fdcbf6d19a3b4c5c82de95ee189942a0d7c3d6","observation_id":"dce6d949-34a6-41ef-955c-9746cc1342c7","resolution":{"observed_at":"2026-08-05T14:33:21.077902Z","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":"2026-08-05T14:33:36.656170Z","title":"Emerg- ing properties in self-supervised vision transformers","venue":null,"work_id":"9e21114f-ccda-4e27-be90-e85daead27ba","year":2021},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:21.155101Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:8d7080d4281c0fbd857fbcbd64eb8fbb0cf16df5ce3dcd2ff8c51d85b3e2b07c","observation_id":"8e6c212a-759c-4aa4-a022-4ad3bbd7cbd6","resolution":{"observed_at":"2026-08-05T14:33:36.686965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:36.516995Z","title":"Wildlifedatasets: An open-source toolkit for ani- mal re-identification","venue":null,"work_id":"dc04bf7a-4b8c-4bbc-a91a-17df9ec82b44","year":2024},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:21.206725Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:d87eba51e0254a33c78281d0e722d1c8ed15ab1ad31507c8cfc89b6ea37f915a","observation_id":"5f4a502e-0d9a-491a-ae6f-14e7b19f5e35","resolution":{"observed_at":"2026-08-05T14:33:36.593109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:36.361582Z","title":"Towards modality- agnostic person re-identification with descriptive query","venue":null,"work_id":"c240398f-62f4-41af-af22-c10f6cf2dfeb","year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:21.297450Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:ee55a0222c6938e8b1fa35cd656232a587c74efdd4cfc2e50e11970af123686f","observation_id":"f69703b0-8b81-4fa4-b2a7-988cc2d35259","resolution":{"observed_at":"2026-08-05T14:33:36.435344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:36.226320Z","title":"Abd-net: Attentive but diverse person re-identification","venue":null,"work_id":"c73cdfe0-bb04-428f-9088-82663a922df8","year":2019},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:21.356719Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:fa470fbcd9aa8462fbe5698556aebf88f476ea6788c903387f82472e93db413f","observation_id":"d8aef626-c5cf-44c0-9399-05ee6da63b0c","resolution":{"observed_at":"2026-08-05T14:33:36.297937Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:36.087928Z","title":"A simple framework for contrastive learning of visual representations","venue":null,"work_id":"de2852de-e3c9-420b-8d50-bc6f57e38084","year":2020},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:21.464895Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:a4248cbcbb4152440fb02acda228a8056d0cbd53a318e14644776a9399958fac","observation_id":"5685db1f-872e-4416-880e-eb90843ed44f","resolution":{"observed_at":"2026-08-05T14:33:36.136518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:35.935960Z","title":"Beyond appearance: a semantic controllable self-supervised learning framework for human-centric visual tasks","venue":null,"work_id":"d491bfbf-7cb2-431a-b598-29705fc1f4cf","year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:21.531355Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:524fa633cc89ef217e5d5e21a85c4cd247a1f5ab16cf9a8e94487269b38020c0","observation_id":"882c8b92-592b-4a77-9b89-999e333f3003","resolution":{"observed_at":"2026-08-05T14:33:36.016733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.04297","last_updated":"2020-03-09T17:56:49Z","snapshot_observed_at":"2026-07-06T09:03:25.467987Z","submitted_at":"2020-03-09T17:56:49Z","title":"Improved Baselines with Momentum Contrastive Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.04297","snapshot_observed_at":"2026-08-05T14:33:21.609294Z","title":"Im- proved baselines with momentum contrastive learning","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:21.609294Z"},"links":{"cited_paper":"/paper/2003.04297","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:4982fb83ae5a9fe1d158856477bafb2f991e54f85d92d797d3ee3a2a732597c0","observation_id":"17f0e0db-9d20-4da4-94b4-6a35c0f20c19","resolution":{"observed_at":"2026-08-05T14:33:21.609294Z","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":"2026-08-05T14:33:35.761284Z","title":"Salience-guided cascaded suppression network for person re-identification","venue":null,"work_id":"5b956c2e-a547-4a6f-9fed-5d6fd3ffef2e","year":2020},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:21.691669Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:5244d94c90f3a0a3298a2fbc475b1580e5b08c2e04c3783b779d05b3abaf30be","observation_id":"de436e2c-eac5-46ce-aa57-706ab49e7818","resolution":{"observed_at":"2026-08-05T14:33:35.821966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:35.567947Z","title":"An empirical study of training self-supervised vision transformers","venue":null,"work_id":"cdca6c1e-994b-4dc4-bc63-96873ee62efe","year":2021},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:21.746003Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:87aa6a97f53f14b72c169a8f3ce4a6d6dec66d38e0012d387abeb3f629625424","observation_id":"21154bb1-abe3-44fb-88d9-b5f44665f73b","resolution":{"observed_at":"2026-08-05T14:33:35.650610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:35.444653Z","title":"Arcface: Additive angular margin loss for deep face recog- nition","venue":null,"work_id":"e13b7975-209d-4885-ba84-f281cc113baf","year":2019},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:21.846865Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:d0a91302e28a20ced6b88b147e4ccdf248a578b763861de4b321046cd9a91c11","observation_id":"12a20d52-7779-4da3-8747-d1ced3bcba15","resolution":{"observed_at":"2026-08-05T14:33:35.469804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.00234","last_updated":"2024-10-05T11:47:02Z","snapshot_observed_at":"2026-07-06T14:36:25.690733Z","submitted_at":"2022-12-31T15:57:09Z","title":"A Survey on In-context Learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.00234","snapshot_observed_at":"2026-08-05T14:33:21.910924Z","title":"A survey on in-context learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:21.910924Z"},"links":{"cited_paper":"/paper/2301.00234","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:6c54dcba77bb487877c20b9d63897ccdec8356553c084ddf2111282e07e66fa6","observation_id":"6af9ca58-5b69-4814-9ffb-863529a8b8e9","resolution":{"observed_at":"2026-08-05T14:33:21.910924Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-10T01:12:16.468283Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-05T14:33:22.006950Z","title":"An image is worth 16x16 words: Trans- formers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:22.006950Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:c9fb152421438422d3d14517a651fcbea5d1b8da2b1009f0a65564070dba00dd","observation_id":"ac0ef35b-17e5-4b51-a419-4be0b4240361","resolution":{"observed_at":"2026-08-05T14:33:22.006950Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12736","last_updated":"2024-07-17T08:13:02Z","snapshot_observed_at":"2026-08-05T09:28:39.799995Z","submitted_at":"2024-03-19T13:53:37Z","title":"Towards Multimodal In-Context Learning for Vision & Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.12736","snapshot_observed_at":"2026-08-05T14:33:22.084330Z","title":"Towards multimodal in-context learning for vision & language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:22.084330Z"},"links":{"cited_paper":"/paper/2403.12736","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:a0e827e9da86e716ccaca4b2a323558682208785fed08dbc825aad5bc2e2cb10","observation_id":"7eeb1dd7-260b-4169-8e99-a7e472d65e32","resolution":{"observed_at":"2026-08-05T14:33:22.084330Z","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":"2026-08-05T14:33:22.175375Z","title":"Model- agnostic meta-learning for fast adaptation of deep networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:22.175375Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:17dd500a4c67428a90ccf15fe79e9b23e4c4ef034806d413a7a8436a7738e445","observation_id":"cc58ae6d-88c3-4c46-a5de-b8508edb3350","resolution":{"observed_at":"2026-08-05T14:33:22.175375Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09344","last_updated":"2023-12-08T21:02:07Z","snapshot_observed_at":"2026-07-06T15:43:07.989730Z","submitted_at":"2023-06-15T17:59:50Z","title":"DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.09344","snapshot_observed_at":"2026-08-05T14:33:22.250094Z","title":"Dreamsim: Learning new dimensions of human visual similarity using synthetic data","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:22.250094Z"},"links":{"cited_paper":"/paper/2306.09344","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:3e4d1c1c98fb1024d928f6e6dd27aa286ca267708dfcaf24e2e0495b74b9bcea","observation_id":"4975dcaa-73ea-462d-b10e-d19dd65711df","resolution":{"observed_at":"2026-08-05T14:33:22.250094Z","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":"2026-08-05T14:33:35.271995Z","title":"Clothes-changing person re-identification with rgb modality only","venue":null,"work_id":"703ac638-f3c7-4df5-8ca6-32aad1b07922","year":2022},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:22.366985Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:a099eb8c8664dcf87c8b7bdbf5605da0333f9c78b6629f9e204bbc4e09865b53","observation_id":"e0fb7770-7c68-4e71-8dcf-fdde37eaa4f9","resolution":{"observed_at":"2026-08-05T14:33:35.338413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:35.093307Z","title":"Language-guided hierarchical fine-grained image forgery de- tection and localization","venue":null,"work_id":"4498db39-9490-431e-9484-92a351aede13","year":2024},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:22.416189Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:a41d081b2acf97d3e1b1f45cb3eaa9c01f8a340c3adaa3718b940297d1fcbb4e","observation_id":"1c9bb2db-001e-47cb-8057-ececc6e3ed09","resolution":{"observed_at":"2026-08-05T14:33:35.210450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:34.936307Z","title":"Masked autoencoders are scalable vision learners","venue":null,"work_id":"7623efff-c0c4-4107-beed-a8237a3ce0ba","year":2022},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:22.512874Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:6d6ccd4fd0c26f03b26d622f40b59e1a7ed1eb1d5976b5043ee80ac597d0c912","observation_id":"9c2f9de1-6c09-4a50-aeb7-f31892185f98","resolution":{"observed_at":"2026-08-05T14:33:35.008651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:34.782543Z","title":"Girshick","venue":null,"work_id":"ee13d843-eb4c-4fb9-8798-332827e0aefd","year":2020},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:22.610967Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:b2ce631b163dd5af01a010cc7d0996f0b470f237c175bba199a205b4342ee22d","observation_id":"cabf5bf5-457a-43be-8a6f-b9340483705f","resolution":{"observed_at":"2026-08-05T14:33:34.857343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:34.665574Z","title":"Transreid: Transformer-based object re- identification","venue":null,"work_id":"61a60cba-25f8-40cd-a0a7-607d243b7bbd","year":2021},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:22.687046Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:25e41bb6b4beade36ba8f167f7db45ba0a724cac0961e696cf6e5c8a847fbffb","observation_id":"35dc64e8-cea9-419d-bc1e-93bb8eadfb2a","resolution":{"observed_at":"2026-08-05T14:33:34.705644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:34.479685Z","title":"Instruct-reid: A multi-purpose person re-identification task with instructions","venue":null,"work_id":"f2b87537-2ae0-4d85-a32a-95d4bbf91e05","year":2024},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:22.772363Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:b30ae05384122985b8cc4c09793dfab137c39437f914388f8b86caaaa7e8e685","observation_id":"01360138-15d5-4af6-b38e-684455a90812","resolution":{"observed_at":"2026-08-05T14:33:34.556905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:34.340749Z","title":"Clothing status awareness for long-term person re-identification","venue":null,"work_id":"32cc9f5a-bd05-491b-84d4-583d5f1c7341","year":2021},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:22.877070Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:c4f12391c180a44c1acdf3e2451419adf95470eedf3b7d16aa7dc172d714b9b2","observation_id":"fbf5fa29-6bdf-445b-8b66-b2232440fa73","resolution":{"observed_at":"2026-08-05T14:33:34.403345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:34.185043Z","title":"Learning representation for clustering via prototype scattering and positive sampling","venue":null,"work_id":"99253b5f-2026-43f1-a149-6adfc3d51041","year":2022},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:22.969835Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:0a56351553602b1b6128c67638c5e00920cfc3041219832ad0b3ca464c6d9b0e","observation_id":"f429d04c-8345-4875-8145-a82bee19f5be","resolution":{"observed_at":"2026-08-05T14:33:34.245084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:34.011612Z","title":"Openclip, July","venue":null,"work_id":"46482f63-b83a-4ab8-90fe-a25d27146560","year":null},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:23.067714Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:031651bece037ff6a22a2fa69299288a6b306c9ad12dbff4c22f44c977dba315","observation_id":"06bf690c-790c-497e-b735-750b58012235","resolution":{"observed_at":"2026-08-05T14:33:34.101560Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:23.254062Z","title":"Visual prompt tuning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:23.254062Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:518423a49e9a3ac2ec0bcf9f8312028d29713631924b9960e05fef2b45312f8c","observation_id":"9e85b317-1f11-4415-be9c-026f61e1b2d8","resolution":{"observed_at":"2026-08-05T14:33:23.254062Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16334","last_updated":"2024-12-20T20:46:48Z","snapshot_observed_at":"2026-08-08T22:58:53.412099Z","submitted_at":"2024-12-20T20:46:48Z","title":"DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16334","snapshot_observed_at":"2026-08-05T14:33:23.363463Z","title":"Dinov2 meets text: A unified framework for image-and pixel-level vision- language alignment","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:23.363463Z"},"links":{"cited_paper":"/paper/2412.16334","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:f0d7f895231e8d8f811d5c6fd62b2def9ad7fac0d5e563317f1a753024c90afa","observation_id":"6ef7d2ae-72c4-48a2-98f4-d893b79ba297","resolution":{"observed_at":"2026-08-05T14:33:23.363463Z","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":"2026-08-05T14:33:33.712402Z","title":"Supervised contrastive learning","venue":null,"work_id":"5805e9f6-a7d5-4d4f-96b9-ee52733cc40b","year":2020},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:23.474748Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:55b98f87d95b595015d16e6c88323d7f1331f92da10d220dff77a6fa642c804b","observation_id":"37d775d9-23b0-4511-af15-2c72716276f8","resolution":{"observed_at":"2026-08-05T14:33:33.795692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:23.580205Z","title":"Adaface: Quality adaptive margin for face recognition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:23.580205Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:431346d1bdffbc3ef65a75898a443754c696a10fd6748fd4b9e25e820a56ba2c","observation_id":"dd7db483-1267-4486-afa9-f0405f36e35b","resolution":{"observed_at":"2026-08-05T14:33:23.580205Z","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":"2026-08-05T14:33:33.586843Z","title":"Sapiensid: Foundation for human recognition","venue":null,"work_id":"1c624771-4364-4587-aa5d-3603c8062d89","year":2025},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:23.642231Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:c3e6b961a2902c4cc3c6bbfe5e50d620b1bd3c7bf97cf9463422ab992700be47","observation_id":"739756a6-2ecf-45ed-bb5f-6b925b9b6ad7","resolution":{"observed_at":"2026-08-05T14:33:33.646576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:33.420862Z","title":"Are these the same apple? comparing images based on object intrinsics","venue":null,"work_id":"afa3ec42-8d77-41fa-8f99-ca6453f1a48b","year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:23.769970Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:6c4b05beeb925a99fd5144d82329d9af915edf0737a515b5e4d8f5ac8af24ed3","observation_id":"824ea9c5-76c7-436d-9aea-1c73f9dec516","resolution":{"observed_at":"2026-08-05T14:33:33.504675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:33.276343Z","title":null,"venue":null,"work_id":"9db0fd50-5d5d-4373-aa8c-78752ea94cc1","year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:23.856835Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:15c81a632dc0d92ca28a2ef9158ca35f8638ec45a4950543b8a19dac0ba33c60","observation_id":"0614ade7-4ab2-4e80-99a1-dcf894cb536d","resolution":{"observed_at":"2026-08-05T14:33:33.339722Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:33.100214Z","title":"Blip- 2: Bootstrapping language-image pre-training with frozen image encoders and large language models","venue":null,"work_id":"3378bb3a-38df-4bdf-beeb-14b445242448","year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:23.918349Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:793b0e6cd916c5f80985a3ed05d4a3b724fb12f7f613aac8691b2435079dc104","observation_id":"64ce78bb-71eb-43e1-b8d2-0a6f9f34a224","resolution":{"observed_at":"2026-08-05T14:33:33.171131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:32.894394Z","title":"Farsight: A physics-driven whole-body biometric sys- tem at large distance and altitude","venue":null,"work_id":"897ebe40-97b2-43c0-84cc-d3f827f85431","year":2024},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:23.989923Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:44578a59af6f0ec242e1da9f99a84b84fbf8bd71c415a846a1aecbd6049652b0","observation_id":"5d4dbc17-60d1-4267-9751-0383c7faac1f","resolution":{"observed_at":"2026-08-05T14:33:32.982958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:32.730668Z","title":"Learning clothing and pose invariant 3d shape representa- tion for long-term person re-identification","venue":null,"work_id":"3ee84fc2-5ed2-4307-b725-b10c55226b3e","year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:24.097580Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:ab854ad8a55593b6250e36c9802d789c9686832ea4e8a2d7ad4c236d5fccd350","observation_id":"6acfe28c-efdf-45e5-89ca-40f1eab0fa23","resolution":{"observed_at":"2026-08-05T14:33:32.820111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:32.524509Z","title":"Distilling clip with dual guidance for learning discriminative human body shape representation","venue":null,"work_id":"90a68231-8e94-4680-8d47-7f44163278fc","year":2024},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:24.170801Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:8394811be7b81d9e0559ccec75056e992d0a83f43c7dcac5fab58765074f62f5","observation_id":"02b06576-5553-4014-9e41-ecc8060eb9db","resolution":{"observed_at":"2026-08-05T14:33:32.626852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:24.268983Z","title":"Visual instruction tuning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:24.268983Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:cfe12b91a359eb4944f45f6b65e0708c674e6e9ebdb22589d4505defb10a6b55","observation_id":"56c35776-9a80-4236-a17f-477fb5c65259","resolution":{"observed_at":"2026-08-05T14:33:24.268983Z","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":"2026-08-05T14:33:32.329554Z","title":"Learning memory-augmented unidirectional metrics for cross-modality person re-identification","venue":null,"work_id":"e30ce66c-fcca-43be-a65c-78da88191508","year":2022},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:24.374197Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:c15efce8eb53904a81eb7e8fd68cea7bb65c243c7d4f85686a2f2651cf8a1059","observation_id":"1c9dbda9-62a1-43e7-9743-709aa181a93c","resolution":{"observed_at":"2026-08-05T14:33:32.411544Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:32.153309Z","title":"Multiple instance learning via iterative self-paced supervised contrastive learning","venue":null,"work_id":"82535e19-1a0e-4021-997d-517162d33aac","year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:24.511093Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:3959d30c2abeb075f383159d1c6ea64bd6524f2794792bf125036396a196742e","observation_id":"7d483f1e-52df-4a86-8c16-f488ac9ae4c3","resolution":{"observed_at":"2026-08-05T14:33:32.261055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:24.640931Z","title":"Grounding dino: Marrying dino with grounded pre-training for open-set object detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:24.640931Z"},"links":{"cited_paper":"/paper/2303.05499","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:c140d54c2a3afcd6f64ba9d8db555901953f1d583fd1a5cdffe5a201f3c7298a","observation_id":"97990836-8d08-455f-b9a2-528590c4d124","resolution":{"observed_at":"2026-08-05T14:33:24.640931Z","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":"2026-08-05T14:33:31.966354Z","title":"A deep learning-based approach to progressive vehicle re- identification for urban surveillance","venue":null,"work_id":"48d64969-2cf6-4cc5-a606-496e28da3919","year":2016},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:24.719533Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:c032c0fc819498c0021ff027a669a53db8345136794eafdc06f9a1f37eb2614c","observation_id":"9d58e4da-6892-49e4-b0ba-3aeb883c8225","resolution":{"observed_at":"2026-08-05T14:33:32.057542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:31.794686Z","title":"Bag of tricks and a strong baseline for deep person re-identification","venue":null,"work_id":"f1e80b79-7005-4628-8b28-811076bb6e97","year":2019},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:24.881818Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:f201c8c6eca01cc65860da0b78c09030375667dee53d1d2bcb5da9ca9af0499e","observation_id":"ec73fffe-1836-4324-b474-bc51a53140be","resolution":{"observed_at":"2026-08-05T14:33:31.848323Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:31.657900Z","title":"Boult, Anderson Rocha, Haidong Zhu, Zhaoheng 10 Zheng, Ram Nevatia, Zaigham Randhawa, Sinan Sabri, and Gianfranco Doretto","venue":null,"work_id":"4dd5c9d2-220a-4dc1-92ca-031e7e9022ee","year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:24.977915Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:cbfa388e00b2dfc2a194509db20076f6d91b83a345b58c0c7841f23567071b6f","observation_id":"9f586859-c5f3-4b2f-8b0e-e40fa867ca71","resolution":{"observed_at":"2026-08-05T14:33:31.747441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:31.518272Z","title":"Deep metric learning via lifted structured feature embedding","venue":null,"work_id":"efae1d53-c38b-4dbd-beeb-53528eb088bb","year":2016},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:25.062602Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:91922db488eaebefee0c86c1b147a3966ac939fcce80a3b246c43e8378b65850","observation_id":"b9c31220-91d9-4600-b647-d1bd99889536","resolution":{"observed_at":"2026-08-05T14:33:31.611963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-09T18:02:17.307812Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-05T14:33:25.171660Z","title":"Dinov2: Learning robust visual features without supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:25.171660Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:1e9ff943e5b0d09abf677675250339cfb223d995d582b937c2c179fa5c18cf8b","observation_id":"770e58c8-9cf8-4a3f-97c8-5dbaca8972ca","resolution":{"observed_at":"2026-08-05T14:33:25.171660Z","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":"2026-08-05T14:33:31.369076Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":"b1993f19-5eff-4c10-af56-1c237e8eda09","year":2021},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:25.258436Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:bd7f829d58ffabf29f8eb1fda8317e78b14afe59faf125584097d491e1818ca8","observation_id":"2415ae62-9510-4720-a503-de48104b93dd","resolution":{"observed_at":"2026-08-05T14:33:31.438225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00714","last_updated":"2024-10-28T16:37:57Z","snapshot_observed_at":"2026-07-06T18:55:41.459417Z","submitted_at":"2024-08-01T17:00:08Z","title":"SAM 2: Segment Anything in Images and Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00714","snapshot_observed_at":"2026-08-05T14:33:25.338262Z","title":"Sam 2: Segment anything in images and videos","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:25.338262Z"},"links":{"cited_paper":"/paper/2408.00714","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:249ceeb45c356e767223e663cc43dd7a9b9eb3cbedb73f0324e8bf651ab8c0cf","observation_id":"0a3a61ca-837e-4d36-b3f5-ead250f13732","resolution":{"observed_at":"2026-08-05T14:33:25.338262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04592","last_updated":"2021-01-24T22:19:30Z","snapshot_observed_at":"2026-08-07T09:28:19.837584Z","submitted_at":"2020-10-09T14:18:53Z","title":"Contrastive Learning with Hard Negative Samples","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.04592","snapshot_observed_at":"2026-08-05T14:33:25.408267Z","title":"Contrastive learning with hard negative sam- ples","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:25.408267Z"},"links":{"cited_paper":"/paper/2010.04592","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:2c2bc4772dd91391baec651c97844a8fbdfd29b16f17bf856c99f6b926c8b416","observation_id":"e912904c-1fe6-476f-8e46-f1d6a7725996","resolution":{"observed_at":"2026-08-05T14:33:25.408267Z","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":"2026-08-05T14:33:31.227009Z","title":"Petface: A large-scale dataset and benchmark for animal identification","venue":null,"work_id":"f3985654-e572-40b5-a305-10f7c7675b93","year":2024},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:25.518904Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:00a2704e6f46251f39213b37ff43fa150c04f51e5346a252dfbb50e39766c5fc","observation_id":"91ab15ec-65ff-4a7a-b24d-683c0f006c18","resolution":{"observed_at":"2026-08-05T14:33:31.284835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:31.012141Z","title":"Prototypi- cal networks for few-shot learning","venue":null,"work_id":"fb8b8a63-d620-4f94-983f-64229ccf6cbe","year":2017},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:25.568590Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:b843743589d1d2df0f7ce9332312aa530cc5344c109276163f9ae8dc5929f40a","observation_id":"198b4d17-f997-404b-b534-7eff7b7fc89d","resolution":{"observed_at":"2026-08-05T14:33:31.143395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:30.776909Z","title":"Hamobe: Hierarchical and adaptive mixture of biometric experts for video-based person reid","venue":null,"work_id":"d7a35a6e-c681-4c11-bce7-0d3b9bae6c6a","year":2025},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:25.622976Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:416de89537f91364f127a988df6b2d6adfde866f7fc8798b49b33630a5a14f29","observation_id":"32d3d79a-fddb-4e1a-ab34-3fa2296016ec","resolution":{"observed_at":"2026-08-05T14:33:30.885285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-05T14:33:25.692891Z","title":"Llama: Open and efficient foundation language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:25.692891Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:9cb4193c7a7309a6bb58fde76f020bccc352749e89ee0a2db39cdc82ea299256","observation_id":"2e6a15b5-7c3a-4209-8396-f4fe96b0259f","resolution":{"observed_at":"2026-08-05T14:33:25.692891Z","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":"2026-08-05T14:33:30.485236Z","title":"Learning discriminative features with multiple granularities for person re-identification","venue":null,"work_id":"9162d25f-0aa5-4b37-924a-aa6467130fa7","year":2018},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:25.726697Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:e199265793cef47b9a5b55e985bee0dce5ab9c3f17fadd4ed5c6508a212adc45","observation_id":"a8dd1bba-e84a-4c8f-821b-4e93e1dd45c2","resolution":{"observed_at":"2026-08-05T14:33:30.602257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:30.211018Z","title":"Panet: Few-shot image semantic segmentation with prototype alignment","venue":null,"work_id":"2ddfbf0f-4a5a-4f73-9285-c75e6d48b4b7","year":2019},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:25.801292Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:d752351f824413b1fcbb32987c10108eb31d9794dddf279f2bc34db17b20ad7b","observation_id":"c0b213fc-0cdc-4bbf-9eb7-2ebb5d4be8d4","resolution":{"observed_at":"2026-08-05T14:33:30.332911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:29.914765Z","title":"Contrastive learning with stronger augmentations","venue":null,"work_id":"bcaac02e-29e6-478f-9e82-875764389b9c","year":2022},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:25.882267Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:401973088b9ada8eb7604e40e28fb899c4c8cf052e6e3ac1df961cacc08ebe55","observation_id":"0e1741ad-e3cb-4b39-adc9-7233c5993ec6","resolution":{"observed_at":"2026-08-05T14:33:30.045165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:29.646512Z","title":"Person transfer gan to bridge domain gap for person re-identification","venue":null,"work_id":"639b6c12-7e8d-4715-81d1-e19f8843570b","year":2018},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:25.998361Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:18da97c71ba9c43ce8a38fcd4d8a94199e494572cf37cee3dce9ade239f69844","observation_id":"249d2f83-b923-40e2-a9e4-c9ef1a8cf2f2","resolution":{"observed_at":"2026-08-05T14:33:29.755713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.07602","last_updated":"2025-03-10T17:58:03Z","snapshot_observed_at":"2026-08-07T17:15:55.158517Z","submitted_at":"2025-03-10T17:58:03Z","title":"DreamRelation: Relation-Centric Video Customization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.07602","snapshot_observed_at":"2026-08-05T14:33:26.062825Z","title":"Dreamrelation: Relation-centric video customization","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.062825Z"},"links":{"cited_paper":"/paper/2503.07602","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:53940df9d90f0855fc2bec14eb3fbbe105c99da1245f85ee36be092b5ddff4eb","observation_id":"10cdcfcc-eb5e-44dc-8634-b581044ddcd3","resolution":{"observed_at":"2026-08-05T14:33:26.062825Z","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":"2026-08-05T14:33:29.408372Z","title":"Unsupervised feature learning via non-parametric instance discrimination","venue":null,"work_id":"3863e6dc-a6d7-40b9-9800-efd4d68c40c7","year":2018},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.146503Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:a1362d1635adf2315bf00bac0069971b33658b0bf85e32842ed99c5ad6403067","observation_id":"a7b61c65-ebf3-4584-9c2e-09391b183949","resolution":{"observed_at":"2026-08-05T14:33:29.552367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:29.147484Z","title":"A fast proximal point method for computing exact wasserstein distance","venue":null,"work_id":"322a700b-f9cb-4d98-88ad-634c86913930","year":null},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.210223Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:1ab833845ff1d80cab2307d7baeec7e81efa8f07d42b5ba2bb1532767bd971e4","observation_id":"04225d21-6f5f-41fa-9d00-bc9b1c4eb9b8","resolution":{"observed_at":"2026-08-05T14:33:29.277679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:28.884286Z","title":"Simmim: A simple framework for masked image modeling","venue":null,"work_id":"7e716d88-3a57-4227-a12c-4efff2209680","year":2022},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.245866Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:857a925101befd9318fc1874525ef5ec6d4202e9a87d9761f6f160ab0af3ef24","observation_id":"2fffda79-154c-434c-8b1c-2f39355724e7","resolution":{"observed_at":"2026-08-05T14:33:28.985030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:28.573783Z","title":"Learning with twin noisy labels for visible-infrared person re-identification","venue":null,"work_id":"de8cb192-98e5-48cb-a5fc-7166947c8089","year":2022},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.298052Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:bd96020237936704144e3548c2bc7565c17ecb9857d7d1f0afca3b1a2e102e0f","observation_id":"2835a855-27ca-4834-97d2-67bda41c5125","resolution":{"observed_at":"2026-08-05T14:33:28.733044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:28.343093Z","title":"Biggait: Learning gait representation you want by large vision models","venue":null,"work_id":"4d7f6db4-ebdf-4d32-b51b-9105fd44065c","year":2024},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.356772Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:4f070edbfd9b960655237d444718d883b7d22279a14c6c82a16a523454c2443a","observation_id":"11e4ee67-e7d5-4056-91e1-f3289e073794","resolution":{"observed_at":"2026-08-05T14:33:28.442813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:28.206834Z","title":"Mvimgnet: A large- scale dataset of multi-view images","venue":null,"work_id":"6da12cef-32c8-4a63-acae-d3d0956d4bc8","year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.426065Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:a05e3c6c225546584f2ef65a65607ca07f1f128050977b4266d4c712bb15d9a2","observation_id":"45bd843f-440d-4b5c-858a-5919b9794e4f","resolution":{"observed_at":"2026-08-05T14:33:28.271803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:28.054461Z","title":"Vehicle re-identification for automatic video traffic surveillance","venue":null,"work_id":"6c654310-cad1-4794-b2ee-8a510ddc59c8","year":2016},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.495105Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:fcbc2b0b5cfe5fa35edd1b2e7739b588ec9d39901c4cb7b886cf8d1a763ac17c","observation_id":"85a6f3c4-9a8b-4dd4-acfa-db8b79568a03","resolution":{"observed_at":"2026-08-05T14:33:28.153667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.18223","last_updated":"2026-03-18T05:34:39Z","snapshot_observed_at":"2026-08-06T23:27:24.356320Z","submitted_at":"2023-03-31T17:28:46Z","title":"A Survey of Large Language Models","version":19},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.18223","snapshot_observed_at":"2026-08-05T14:33:26.562778Z","title":"A survey of large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.562778Z"},"links":{"cited_paper":"/paper/2303.18223","citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:0dfa63b1b277b97485354fff1548686e495058d8535ae19e9c8ad5c507a2478f","observation_id":"8bddb4da-e17d-4a1c-96d1-0d912f1c57d8","resolution":{"observed_at":"2026-08-05T14:33:26.562778Z","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":"2026-08-05T14:33:27.882450Z","title":"Scalable person re-identification: A benchmark","venue":null,"work_id":"9c79a61f-66d0-4567-9887-e0dd0d4d3f01","year":2015},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.650129Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:da39700bbff8396c96d73f69d13ba734d22dc2c54c32d3ad0008d9766974d2a4","observation_id":"e295bcd2-a884-406b-8320-a45df828e17a","resolution":{"observed_at":"2026-08-05T14:33:27.967307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:27.730762Z","title":"Pass: Part-aware self-supervised pre- training for person re-identification","venue":null,"work_id":"cacf7bed-84cd-48cc-b516-545c101f90a2","year":2022},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.733766Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:06c447ed0fea7662f2554276a1d176c3a97931a1e1bdedde58c1995b67c7ebd2","observation_id":"bb262b6a-155a-46b6-86b5-d451fe00c1f0","resolution":{"observed_at":"2026-08-05T14:33:27.786683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:27.577670Z","title":"Aaformer: Auto-aligned transformer for person re-identification","venue":null,"work_id":"e370417a-1a1d-4cb3-a7a3-17bcd52e1ab6","year":2023},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.771100Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:d5687e2105b29adc7f739275e0836f284c63a01a847e30ec11fbbeac0555fc97","observation_id":"5a236ce0-92d7-4416-a446-10c861a6584f","resolution":{"observed_at":"2026-08-05T14:33:27.655915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:27.358612Z","title":"VL-ICL bench: The devil in the details of multimodal in- context learning","venue":null,"work_id":"11e8625c-8986-4e7c-bd0e-adf553823f98","year":2025},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.833750Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:cc308752fadbd7bd9fdb8dc7ab4f2ebf0e0e3f476e598a89f8ce7977b4b99af1","observation_id":"7df139ab-5081-46a7-926d-88233b284973","resolution":{"observed_at":"2026-08-05T14:33:27.475820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:27.211124Z","title":"While ShopID10K is visually observed with occlusion variations, there are no explicit occlusion labels, making it difficult to quantitatively evaluate occlusion robustness","venue":null,"work_id":"19888537-4479-4002-99d8-d48a2eb09e83","year":null},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:26.893881Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:45a48bafebcb2ff7b54c3f951297d485e4f0581b78d462b8faa923fb63aa5eaf","observation_id":"2f38f2db-c4d2-4cff-b879-037ba96638a9","resolution":{"observed_at":"2026-08-05T14:33:27.261579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T14:33:33.871401Z","title":null,"venue":null,"work_id":"9af16910-7907-4488-9dc1-e59233f65c1e","year":null},"citing_paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-05T14:33:23.140440Z"},"links":{"citing_paper":"/paper/2508.21222"},"observation_digest":"sha256:0000adff4b3d8a7c07ec304686df2f3247ba7205358a9f79a8a8e999c6e39fa0","observation_id":"d2bc3b22-e73c-4f1a-bd5d-a86304f6b1ad","resolution":{"observed_at":"2026-08-05T14:33:33.951865Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.21222","last_updated":"2025-08-28T21:24:06Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-05T14:33:19.493614Z","submitted_at":"2025-08-28T21:24:06Z","title":"Generalizable Object Re-Identification via Visual In-Context Prompting"},"reference_resolution":{"displayed":79,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":0,"verified_fuzzy":55},"total_outbound_references":79},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 0 inbound Pith citation observations for arXiv:2508.21222."}