{"as_of":"2026-08-04T19:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:55eea7426fb848e1cecaf433b85fcd3ddbbdda84809622f45b83a06ab94a37f8","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-12T04:53:56.917355Z","state":"measured"},{"denominator":34,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":34,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-12T04:53:56.917355Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-05-12T05:51:24.385703Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"cited_work":{"arxiv_id":"2605.09858","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.09858","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","venue":"cs.CV","work_id":"bdcc9186-116f-4b55-a538-d2ddfd03e642","year":2026},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"cited_paper":"/paper/2605.09858","citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:40f059fcb5af0f799d465df711e468c6677fac6d4065ad5bb060c21b7c007fe7","observation_id":"836c5399-dab8-4fd3-a94b-ba10849137af","resolution":{"observed_at":"2026-05-12T05:51:24.389607Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2605.09858/citation-record","integrity":"/paper/2605.09858/integrity","json":"/paper/2605.09858/citation-record.json","paper":"/paper/2605.09858"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"cited_work":{"arxiv_id":"2605.09858","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.09858","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","venue":"cs.CV","work_id":"bdcc9186-116f-4b55-a538-d2ddfd03e642","year":2026},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"cited_paper":"/paper/2605.09858","citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:40f059fcb5af0f799d465df711e468c6677fac6d4065ad5bb060c21b7c007fe7","observation_id":"836c5399-dab8-4fd3-a94b-ba10849137af","resolution":{"observed_at":"2026-05-12T05:51:24.389607Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"da359e3d-d2dd-43ec-9177-f3fb75f185be","year":null},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:6570b10dffea28bda55e65f078cde461f74397fcf8a4a36b8ea33540893ff85f","observation_id":"d03264ab-97b3-465e-9bc3-6485f56a5d54","resolution":{"observed_at":"2026-05-12T13:06:35.558305Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Problem Setting We formulateclip-level active learningfor multi-frame end- to-end MOT, employing fixed-length clips as the acquisition unit","venue":null,"work_id":"8dd41638-61e2-4242-b657-43c65d8d859a","year":null},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:4c108f43f8da0594667f0bc5543defe2b24a4a233486aeeca27c4db3ee156d91","observation_id":"08649e9e-5984-4b15-9d97-f17e9afe6661","resolution":{"observed_at":"2026-05-12T13:06:35.544905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Due to space con- straints, we report the quantitative comparisons in this sec- tion","venue":null,"work_id":"0e763cf6-35cd-4e07-831b-ea225fdd9123","year":null},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:674b3d3c567bce1b1dee892adb274f3dcc78be48c190241c1f445de10979bde7","observation_id":"1c400d4e-22ff-4f92-8749-854a6c84e7d8","resolution":{"observed_at":"2026-05-12T13:06:35.562768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3b1ca1e1-1200-445b-95fb-70039f264ec4","year":null},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:62fe7833ace6f9b600aa20dad6afb6c2f97c01261cfb6b62d1a1cbeb01668530","observation_id":"0733d55f-8905-44b9-964b-4ef89af166aa","resolution":{"observed_at":"2026-05-12T13:06:35.536059Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Bdd100k: A diverse driving dataset for heterogeneous mul- titask learning","venue":null,"work_id":"9056fcb2-c2e3-4420-8610-f3456d3ce12d","year":2020},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:d484b59547fff9da561640c7c443009e5906c3e7fdeb45d8428e0a57647dfd31","observation_id":"d6b0093f-d2d3-46ab-85c1-4203a1761939","resolution":{"observed_at":"2026-05-12T13:06:35.554044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Sportsmot: A large multi-object tracking dataset in multiple sports scenes","venue":null,"work_id":"fb82e44d-5df1-4fea-80d1-3856b77bdcea","year":2023},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:0dd405403d69749ba00d7f7e9b0fffa151f4b0454f9e68be9ef140d9e64c287d","observation_id":"c40ae8db-b62e-43a8-a519-fcd09c06fcf5","resolution":{"observed_at":"2026-05-12T13:06:35.567440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Trackformer: Multi-object tracking with transformers","venue":null,"work_id":"34c840c5-b3b5-4f3e-ab39-e24a59ae0718","year":2022},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:563e6a04391e0c4c4fbed6e55e03acdfe1cc7f3b50c038282dd79ddbb41f1cdb","observation_id":"d53c68ab-e67f-4666-a719-ddd9607ce3ef","resolution":{"observed_at":"2026-05-12T13:06:35.540237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Motr: End-to-end multiple- object tracking with transformer","venue":null,"work_id":"8531ec8f-ae9c-4530-b6be-677df486346b","year":2022},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:3e477f5e1831e96f8250ebe54428e76a06ed3fcb2c36e01151875916c164054e","observation_id":"fca2d44b-5e9d-473c-9536-b6a30b5fc639","resolution":{"observed_at":"2026-05-12T13:06:35.549567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Memotr: Long-term memory-augmented transformer for multi-object tracking","venue":null,"work_id":"e8bd5844-723a-4076-991f-7771c5269a9c","year":2023},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:499b63a7d2f1dffa0f30d9317eaf4df329daad4aa2c7d54a642c96a347a16eaf","observation_id":"df60be03-3dc7-4e9b-90c5-e67b5d7f0955","resolution":{"observed_at":"2026-05-12T13:06:35.509829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Samba: Synchronized set- of-sequences modeling for multiple object tracking","venue":null,"work_id":"2757a0e4-5420-4192-a5f7-47ecfd899e2e","year":2025},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:cde6b7d4fc85d25c538b680ebcbbc6fbb39aec46b63d1b8af79f93014b2f6b12","observation_id":"4d336360-6022-4719-bc2b-707dcdad65a8","resolution":{"observed_at":"2026-05-12T13:06:35.523377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Cost-effective active learning for deep image classifica- tion","venue":null,"work_id":"2329ce9d-1803-40b4-8e7c-b26a0bfa26df","year":2016},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:f37c4c75007581f818a164db293fdabb8f1cd98f448145a43b7ba21d3fabb0bd","observation_id":"8a8387ee-28de-43ac-9910-2e749c09df71","resolution":{"observed_at":"2026-05-12T13:06:35.532126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.00489","last_updated":"2018-06-01T10:17:23Z","snapshot_observed_at":"2026-07-06T05:53:39.440274Z","submitted_at":"2017-08-01T19:50:53Z","title":"Active Learning for Convolutional Neural Networks: A Core-Set Approach","version":4},"cited_work":{"arxiv_id":"1708.00489","doi":"10.48550/arxiv.1708.00489","metadata_source":"pith","pith_arxiv_id":"1708.00489","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Active Learning for Convolutional Neural Networks: A Core-Set Approach","venue":"stat.ML","work_id":"64b057c0-f8b3-4d7a-9f16-e2880f8b501c","year":2017},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"cited_paper":"/paper/1708.00489","citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:fff3aceed117d86d2592e3b7e443e2e238544c1ee1b11044923fc9f40abd8a1e","observation_id":"04e1564d-931e-44e9-add9-e9dd3d0e4a38","resolution":{"observed_at":"2026-05-12T05:51:24.364966Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.03671","last_updated":"2020-02-24T02:14:51Z","snapshot_observed_at":"2026-07-06T07:59:03.272339Z","submitted_at":"2019-06-09T16:52:09Z","title":"Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds","version":2},"cited_work":{"arxiv_id":"1906.03671","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1906.03671","snapshot_observed_at":"2026-07-04T20:10:07.387819Z","title":"Deep batch active learning by diverse, uncertain gradient lower bounds","venue":null,"work_id":"47c38e6d-d0f3-42e2-9042-f4ea058d4ea8","year":1906},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"cited_paper":"/paper/1906.03671","citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:98ccadb0fa23217d1b8732dc9b1a8bf0c5cc696b67b0fed115bb9c5c2d71fca6","observation_id":"ab80530f-ce73-4028-a90f-49842f525b6c","resolution":{"observed_at":"2026-05-12T05:51:24.369717Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Are all frames equal? active sparse labeling for video action detection","venue":null,"work_id":"0ec2192e-98f5-4a86-bff3-be15d165a5de","year":2022},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:faf6bb792a20a1abbe05bc8bff7377d09e23c4b2e77d44011a7f01600e33665f","observation_id":"22828ab1-6123-4834-84a6-cd1ff2e72f70","resolution":{"observed_at":"2026-05-12T13:06:35.496442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Hybrid active learning via deep clustering for video action detection","venue":null,"work_id":"0751b242-0812-4d1a-8d35-74d62c25f5ec","year":2023},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:87874c95bcc9f265f0b46f08e619064cccf742a5bce97086f4b68c2ec4ff8c6a","observation_id":"fbd37d69-8d8a-451e-bb4b-125c5fe8dd05","resolution":{"observed_at":"2026-05-12T13:06:35.518756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Heterogeneous diversity driven active learning for multi-object tracking","venue":null,"work_id":"0fe5e16f-9c61-4a42-b47e-862a09778592","year":2023},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:329137bbc69370af7ac2631202b3d5beeb4b2087d2ed91a4de16228693a2079e","observation_id":"d14fd5d7-2a28-412b-901e-ac8abdf6b035","resolution":{"observed_at":"2026-05-12T13:06:35.484692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Bytetrack: Multi-object tracking by associating every detection box","venue":null,"work_id":"bb2404e0-cea3-4ea8-a949-5768fbb479bb","year":2022},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:7979873d559655398166142306fc6c3ccd8c67a9450083347ecf5512ebd73ba2","observation_id":"a910be98-d422-4564-9aa5-b086c0ded0c8","resolution":{"observed_at":"2026-05-12T13:06:35.514201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.08430","last_updated":"2021-08-06T03:22:14Z","snapshot_observed_at":"2026-07-06T11:30:06.143581Z","submitted_at":"2021-07-18T12:55:11Z","title":"YOLOX: Exceeding YOLO Series in 2021","version":2},"cited_work":{"arxiv_id":"2107.08430","doi":null,"metadata_source":"pith","pith_arxiv_id":"2107.08430","snapshot_observed_at":"2026-07-04T19:20:06.601231Z","title":"YOLOX: Exceeding YOLO Series in 2021","venue":"cs.CV","work_id":"112b3cd9-8fe6-49fe-bbaa-90a3f46045c7","year":2021},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"cited_paper":"/paper/2107.08430","citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:1b56bcdb59941512067217368d0cee27aa40419b50ecdecc6b53dac9a14264ee","observation_id":"3e417bbb-44f6-4260-a54a-dd2ba58231ba","resolution":{"observed_at":"2026-05-13T10:31:31.716715Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Simple online and realtime tracking","venue":null,"work_id":"ff379d15-19b6-40a8-af3d-34a05ee5c00f","year":2016},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:4b83a908011e05ff4858c0b46d1939a0504cd8a3ab31ba1d2d455cc6f9cc444a","observation_id":"56fa849d-3a1e-451d-831b-cfd2173cc229","resolution":{"observed_at":"2026-05-12T13:06:35.488458Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Fairmot: On the fairness of detection and re- identification in multiple object tracking","venue":null,"work_id":"5385f9ed-a37b-4f76-b968-67b030ff6a2a","year":2021},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:27937a9a8306e84080d4d0d009c32d19de0bc82ce671cb773b2b432cde7a13c5","observation_id":"f32059ed-f8d7-4fe3-8ae2-b950e46afd8c","resolution":{"observed_at":"2026-05-12T13:06:35.479475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1603.00831","last_updated":"2016-05-03T23:55:38Z","snapshot_observed_at":"2026-08-03T05:59:06.882015Z","submitted_at":"2016-03-02T19:07:56Z","title":"MOT16: A Benchmark for Multi-Object Tracking","version":2},"cited_work":{"arxiv_id":"1603.00831","doi":null,"metadata_source":"pith","pith_arxiv_id":"1603.00831","snapshot_observed_at":"2026-07-10T02:26:43.120212Z","title":"MOT16: A Benchmark for Multi-Object Tracking","venue":"cs.CV","work_id":"21cb450c-bdab-4f4d-9bcd-42e02d422eb3","year":2016},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"cited_paper":"/paper/1603.00831","citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:3826999617025c56c243023d88c4b8bf74eefa54366178613a74cf9b808036c4","observation_id":"816f3c72-a028-40d4-af6b-44c1db143e9e","resolution":{"observed_at":"2026-05-12T05:51:24.381405Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Mamba: Linear-time sequence mod- eling with selective state spaces","venue":null,"work_id":"9f2f4479-7f6a-4337-a865-3aec7367c293","year":2024},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:b27a8641ccac9e6f57fadc49fe527cabddf8f466c54b9d1dcf8a855f2bf2c547","observation_id":"8aaee0a4-f738-4c44-a792-1c79134319bf","resolution":{"observed_at":"2026-05-12T13:06:35.451950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Spamming labels: Efficient annotations for the trackers of tomorrow","venue":null,"work_id":"549e457f-8d8d-45ed-9fcd-93d358816c39","year":2024},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:34ef4497b842a3de13e8d4d8ab717730af49a6a24b668447c56df8d781b08205","observation_id":"f9960cfd-6b81-4a53-bf30-f8cb715d352f","resolution":{"observed_at":"2026-05-12T13:06:35.447609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Plug and play active learning for object detection","venue":null,"work_id":"507503a9-275e-4771-9407-93dfec761e54","year":2024},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:15fa6557b464f4782d82910c67fdc5c4c1aad222a3129c57c5bab9a761e044cf","observation_id":"af54d8ff-fe97-40d3-95b0-2b31decca530","resolution":{"observed_at":"2026-05-12T13:06:35.456120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Active domain adaptation with false negative prediction for object detection","venue":null,"work_id":"8cb55db6-42a4-4566-babb-338f0d2d6ac0","year":2024},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:60e8258cef87fed5493a893a0e2ce29f0644962b5d3f89dbd0a9cbdcd7d6ec90","observation_id":"66787f34-0a2a-48d1-9a24-f6a69ce0d858","resolution":{"observed_at":"2026-05-12T13:06:35.474741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Hota: A higher order metric for evaluating multi-object tracking","venue":null,"work_id":"d0c64a43-f968-4a90-a1a4-a2ab3a3deff1","year":2021},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:02f1042a7ada34ad93bb099f123fa954494ed480c80a654ba9973702464a2142","observation_id":"f3bb4072-29e2-47ae-ba53-60fc5f51543e","resolution":{"observed_at":"2026-05-12T13:06:35.505768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Performance measures and a data set for multi-target, multi-camera tracking","venue":null,"work_id":"a9f01f0e-6b05-4a58-9ab7-6f302a226869","year":2016},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:f08bfaa0d9456972d4a892f907189e1f01f9b370a1f44f1d558512c202be824c","observation_id":"9fa07879-53e6-4c4a-aa0a-b9109e7cc703","resolution":{"observed_at":"2026-05-12T13:06:35.492339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Making your first choice: to address cold start problem in medical active learn- ing","venue":null,"work_id":"61dfd8c4-ca11-4656-8f1c-d89439f4a873","year":2024},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:fec852049b040e8a1d5a97eac1816ff846705b2ee7260cf4a6b3b86e755b4c9e","observation_id":"6be90cd0-ec22-44cd-8f44-999476bf8c52","resolution":{"observed_at":"2026-05-12T13:06:35.470556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Dancetrack: Multi-object tracking in uniform appearance and diverse motion","venue":null,"work_id":"b2a8cc0d-5de0-4d60-bc47-cc3c02f4aba2","year":2022},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:2f5e27badab9edb192b2b1fdf6b68555de7f93aeae70be08fe93259f07bf18dd","observation_id":"e7afc6ab-f40b-4737-8577-629bfc8ec1f2","resolution":{"observed_at":"2026-05-12T13:06:35.460779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Memotr: Long-term memory- augmented transformer for multi-object tracking","venue":null,"work_id":"24ac2210-8060-4a97-861e-e45b945e99fd","year":2023},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:559bddad183fe09413cf5ec8b45b9326892c7240fd78d0b2aac0de4f3b1aa4a8","observation_id":"151f3ef1-ac11-4c18-b356-d77a3663644c","resolution":{"observed_at":"2026-05-12T13:06:35.501361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Samba: Synchronized set-of- sequences modeling for multiple object tracking","venue":null,"work_id":"78252752-80f8-4ced-ab4b-d8db7cf4a2be","year":2025},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:e496aebe125f6f91c999f826ece5dab5e64c7c5caf5db65dafbc8d5a5a28aebc","observation_id":"55b05229-db82-4556-ba80-abb74e458bff","resolution":{"observed_at":"2026-05-12T13:06:35.466502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Making your first choice: to address cold start problem in medical active learning","venue":null,"work_id":"81ff2001-b325-41d3-beb7-2231646cc316","year":2024},"citing_paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:56.917355Z"},"links":{"citing_paper":"/paper/2605.09858"},"observation_digest":"sha256:a17fb6cdff8d8f1e4f8f7a1fc7b73ea0ca68d97b4602831a588ce00ffa59c386","observation_id":"26a8538b-034a-4d22-8092-71de106d22c3","resolution":{"observed_at":"2026-05-12T13:06:35.527653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.09858","last_updated":"2026-05-11T01:33:35Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-02T11:34:44.156869Z","submitted_at":"2026-05-11T01:33:35Z","title":"Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":2,"verified_exact":4,"verified_fuzzy":26},"total_outbound_references":33},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2605.09858."}