{"as_of":"2026-08-11T01:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:92f0dfd6900110bc551a6424e8979e3063a0cd328f1555a8c5c71abab5434bd0","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:38:17.192543Z","state":"measured"},{"denominator":78,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":78,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T21:00:49.427937Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T15:59:56.411212Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.23783","snapshot_observed_at":"2026-08-04T21:00:49.427937Z","title":"Mamba-fetrack v2: Revisiting state space model for frame- event based visual object tracking,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-09T18:32:21.415897Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.427937Z"},"links":{"cited_paper":"/paper/2506.23783","citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:271e8d1b2c32e479cf22680228f75e0cfd0c5cce26bb20f7cf3c1c931b0f51ef","observation_id":"f6ab3875-5183-435d-8b36-0d9e28f40f44","resolution":{"observed_at":"2026-08-04T21:00:49.427937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"cited_work":{"arxiv_id":"2506.23783","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.23783","snapshot_observed_at":"2026-07-04T15:59:56.411212Z","title":"Mamba-fetrack v2: Revisiting state space model for frame- event based visual object tracking,","venue":null,"work_id":"992311f4-c591-4ecc-a761-48deec09169b","year":2025},"citing_paper":{"arxiv_id":"2606.23078","last_updated":"2026-06-22T09:26:14Z","snapshot_observed_at":"2026-08-06T04:35:50.165443Z","submitted_at":"2026-06-22T09:26:14Z","title":"A Systematic Survey on Event Camera Representation Learning","version":1},"reference_index":154,"source":"pdf_text","source_observed_at":"2026-06-26T06:33:12.448178Z"},"links":{"cited_paper":"/paper/2506.23783","citing_paper":"/paper/2606.23078"},"observation_digest":"sha256:fbff3e1c28af35a760fc3be7a7f10da65479d492c68cb808227fbf7c9a4f4ae4","observation_id":"e7b0533a-d891-4fb5-a1d9-10fbb011fe7b","resolution":{"observed_at":"2026-07-04T12:39:48.874077Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"cited_work":{"arxiv_id":"2506.23783","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.23783","snapshot_observed_at":"2026-07-04T15:59:56.411212Z","title":"Mamba-fetrack v2: Revisiting state space model for frame- event based visual object tracking,","venue":null,"work_id":"992311f4-c591-4ecc-a761-48deec09169b","year":2025},"citing_paper":{"arxiv_id":"2606.26455","last_updated":"2026-06-24T23:34:06Z","snapshot_observed_at":"2026-08-07T12:04:43.181184Z","submitted_at":"2026-06-24T23:34:06Z","title":"Active Adversarial Perturbation-driven Associative Memory Retrieval for RGB-Event Visual Object Tracking","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T01:12:13.212022Z"},"links":{"cited_paper":"/paper/2506.23783","citing_paper":"/paper/2606.26455"},"observation_digest":"sha256:2d9921cf3ecf19daddd97ad150ecd19391c31c2e41adba0afcae57dd583107cb","observation_id":"c9757c90-16c1-48a0-a2f4-59a8a480a557","resolution":{"observed_at":"2026-07-04T15:59:56.412724Z","resolver_source":"arxiv_id","status":"verified_exact"},"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"}}],"links":{"evidence":"/evidence","html":"/paper/2506.23783/citation-record","integrity":"/paper/2506.23783/integrity","json":"/paper/2506.23783/citation-record.json","paper":"/paper/2506.23783"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:38:30.746231Z","title":"Augment one with others: Gener- alizing to unforeseen variations for visual tracking,","venue":null,"work_id":"91cc9310-91b7-4d98-9d33-57242209f140","year":2025},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:09.612889Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:6b5449f381af7de725f17344e663622d077e66b3dd18bac36e1a957f86b8ded1","observation_id":"f810aece-d2b6-4a3a-aca6-29d46cc3b64d","resolution":{"observed_at":"2026-08-06T21:38:30.869931Z","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":"2026-08-06T21:38:30.611764Z","title":"Improving visual object tracking through visual prompting,","venue":null,"work_id":"edc28f17-8685-4356-ad76-1aa632293426","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:09.717242Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:d5453ae16058bf66d8ef6f9eea98c45e1c1e43041f29ef71eb3e22bcd98f9a7c","observation_id":"a6bac2f0-40d1-402e-b0d5-35ca559d4ba9","resolution":{"observed_at":"2026-08-06T21:38:30.678410Z","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":"2026-08-06T21:38:30.353852Z","title":"Local fine-grained visual tracking,","venue":null,"work_id":"b55433c2-2df7-4563-b63e-b1443070f030","year":2025},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:09.827729Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:5f78b8de22ea74a33365ab60282f70a02208d0155f70816ef5165022892dcb53","observation_id":"988c72fa-6432-4813-96e3-613372a5cb3d","resolution":{"observed_at":"2026-08-06T21:38:30.446348Z","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":"2026-08-06T21:38:30.029192Z","title":"Linker: Learning long short-term associations for robust visual tracking,","venue":null,"work_id":"4ee0dea2-8662-47ae-bdc9-4cdeee175bde","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:09.896020Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:a621315c1bb6e5864355b02baf655bc55c936e4cfdf603d5bda83993412cd375","observation_id":"02416867-b305-4828-9326-513c5a69c2d3","resolution":{"observed_at":"2026-08-06T21:38:30.174690Z","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":"2211.11010","last_updated":"2024-01-08T13:27:47Z","snapshot_observed_at":"2026-08-10T06:23:50.548877Z","submitted_at":"2022-11-20T16:01:31Z","title":"Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.11010","snapshot_observed_at":"2026-08-06T21:38:10.053888Z","title":"Revisiting color-event based tracking: A unified network, dataset, and metric,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:10.053888Z"},"links":{"cited_paper":"/paper/2211.11010","citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:13a5c948014b99dd45fa9b58f6c1e0b9dc0a4cf7c8bf3f06211b62e963ac56be","observation_id":"907e7bab-5cee-44a1-a9ef-a6e0ade9ebdb","resolution":{"observed_at":"2026-08-06T21:38:10.053888Z","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":"2026-08-06T21:38:29.794418Z","title":"Frame- event alignment and fusion network for high frame rate tracking,","venue":null,"work_id":"03363cc9-85eb-4531-9f74-005826d9a1fc","year":2023},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:10.184661Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:007919f8e2719a517f7d02d199b6871c5ae4f83bddc31a96d169d518e3ccffd1","observation_id":"e7fe6e00-6fed-4c50-8801-617dea0aa812","resolution":{"observed_at":"2026-08-06T21:38:29.918966Z","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":"2026-08-06T21:38:10.328705Z","title":"Gradient-based learning applied to document recognition,","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:10.328705Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:6375f83ce335618e125c3b8f1dc9d2e9250d39d9f079b9c0047f29bcfa55efb0","observation_id":"a3c17a08-a1e1-41de-97a2-6a5e35ca06fb","resolution":{"observed_at":"2026-08-06T21:38:10.328705Z","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":"2026-08-06T21:38:29.554823Z","title":"Atom: Accurate tracking by overlap maximization,","venue":null,"work_id":"bdeea0a4-7576-48e1-9f00-3678ded87915","year":2019},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:10.445352Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:2e18c4c581c3e20948d5e6bc7b4187999a74013c62fe86ba222e1882cd58604b","observation_id":"45b6e34d-d70a-44a8-84a1-f43ed6425d17","resolution":{"observed_at":"2026-08-06T21:38:29.646858Z","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":"2026-08-06T21:38:29.397292Z","title":"Learning discrim- inative model prediction for tracking,","venue":null,"work_id":"fe442563-7341-4e1e-b375-fcaad39f9e4d","year":2019},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:10.578921Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:f6c53d420332ac24880648136426ce91a7039bc4a016e53c25b12d41e382e5eb","observation_id":"46db73e7-8f7e-4ebb-97c9-883421c25e2f","resolution":{"observed_at":"2026-08-06T21:38:29.449551Z","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":"2026-08-06T21:38:29.227561Z","title":"Ocean: Object-aware anchor-free tracking,","venue":null,"work_id":"0f9d8f38-ab31-4b69-8f67-f9b5bab7c406","year":2020},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:10.746199Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:c0bd1c0ecc22ebd7a1d681cfae57859612ef92b8f664e3fdf5d4a167021fbc52","observation_id":"88afb94a-ff12-4c43-b723-d9be30e97fb0","resolution":{"observed_at":"2026-08-06T21:38:29.290269Z","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":"2026-08-06T21:38:29.026988Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":"bfeaee7c-a7cc-4f50-8ef3-c671febd6910","year":2020},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:10.903294Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:4c14c3ab6c4a1dfabe2a8cb5804d0b37c00e74e18f0343e6b9b7530b15c38926","observation_id":"abdf0377-85e9-44ec-9c97-b2e7021cbf36","resolution":{"observed_at":"2026-08-06T21:38:29.072931Z","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":"2026-08-06T21:38:28.857039Z","title":"Learning spatio-temporal transformer for visual tracking,","venue":null,"work_id":"7c9650ea-958a-4bfd-b9d4-97c622be53de","year":2021},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:11.049998Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:ec9d425f63c9097489583b04b98c825d849a0c017d5315421384dc70e9e13e0c","observation_id":"91cd0252-ffaa-42a3-a187-e0f395380f04","resolution":{"observed_at":"2026-08-06T21:38:28.926534Z","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":"2026-08-06T21:38:28.662766Z","title":"Aiatrack: Attention in attention for transformer visual tracking,","venue":null,"work_id":"031f5556-673d-40be-a4a8-d5ad34c5558b","year":2022},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:11.205735Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:5132f0e4b4bdc090f6860c9e6b7d7979c2a072a4e8869110e2162592828db854","observation_id":"c2c69cf4-a723-49bf-b688-8aca724b5c4f","resolution":{"observed_at":"2026-08-06T21:38:28.745829Z","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":"2026-08-06T21:38:11.315330Z","title":"Swintrack: A simple and strong baseline for transformer tracking,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:11.315330Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:422145c825cffd96947c7b00d651402d0843aa45b5c2df42456e325e7f26a0e4","observation_id":"4b1a8e8b-1637-4a75-b286-afce726a352c","resolution":{"observed_at":"2026-08-06T21:38:11.315330Z","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":"2026-08-06T21:38:28.489326Z","title":"Efficiently modeling long sequences with structured state spaces,","venue":null,"work_id":"328b35d2-876e-49c9-ad4c-ab620f9f4d4f","year":2022},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:11.386263Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:50f8f0a5207e60a456da2beccc9ca58639c7b04359fdf6dce1267b3948adcace","observation_id":"0be70784-e476-481a-bddc-53679047db8a","resolution":{"observed_at":"2026-08-06T21:38:28.573319Z","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":"2026-08-06T21:38:28.279878Z","title":"Vision mamba: Efficient visual representation learning with bidirectional state space model,","venue":null,"work_id":"d382b676-7a98-4ad8-968d-eda9c4c143b6","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:11.485231Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:9a485d9adfa2c59cee92b53b0af16e8822da5ba6e0331815b3df97a26f4016bc","observation_id":"be544064-da59-4a99-8071-3ebcf12b2815","resolution":{"observed_at":"2026-08-06T21:38:28.403273Z","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":"2026-08-06T21:38:11.585160Z","title":"Vmamba: Visual state space model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:11.585160Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:7d3ac5576492584946454c53fda77eef974022739918752512e882bdb5431f5b","observation_id":"7bbcffb5-9233-4771-8e86-32c6652e9938","resolution":{"observed_at":"2026-08-06T21:38:11.585160Z","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":"2026-08-06T21:38:11.695197Z","title":"Mambavision: A hybrid mamba- transformer vision backbone,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:11.695197Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:53af6f235290d31e821b28c0e4ab652061a1ae9b8c260366102dd15a274f4e69","observation_id":"88631db8-b709-4b23-8030-26e6d68129d0","resolution":{"observed_at":"2026-08-06T21:38:11.695197Z","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":"2026-08-06T21:38:28.003823Z","title":"Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language processing,","venue":null,"work_id":"3702bb40-953f-450c-9a75-ceb67403ed2f","year":2023},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:11.793466Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:60a36b726e0ce843fb2196630e933dd8e462154f54c0a6b1e46b7ff7027240ae","observation_id":"9d4cbfe3-d586-4d0f-91fa-453b80f186e3","resolution":{"observed_at":"2026-08-06T21:38:28.131618Z","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":"2026-08-06T21:38:27.808379Z","title":"Mambairv2: Attentive state space restoration,","venue":null,"work_id":"a72d7fc7-2ada-4d04-9311-f0fdd84f79bb","year":2025},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:11.870564Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:66092492f398392c80cd23638108ccb43a2e71b6a5b8b0412517912ea8fc98d6","observation_id":"3f1a18fe-9a7d-45cd-9cb8-4f920e931d10","resolution":{"observed_at":"2026-08-06T21:38:27.934415Z","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":"2026-08-06T21:38:11.952498Z","title":"Mamba- fetrack: Frame-event tracking via state space model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:11.952498Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:582cd9f881540dd1e33dfd784055bb67b9db54f90cbffd0d0e6b6b1176cc5e88","observation_id":"ffcf2fac-475b-462f-8528-c8462438774f","resolution":{"observed_at":"2026-08-06T21:38:11.952498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14200","last_updated":"2024-05-31T11:09:59Z","snapshot_observed_at":"2026-08-10T21:45:44.764179Z","submitted_at":"2024-05-23T05:58:10Z","title":"Awesome Multi-modal Object Tracking","version":2},"cited_work":{"arxiv_id":"2405.14200","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.14200","snapshot_observed_at":"2026-08-06T21:38:17.649389Z","title":"Awesome Multi-modal Object Tracking","venue":"cs.CV","work_id":"4a402685-801f-4bf6-a688-1d400063c38e","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:12.033171Z"},"links":{"cited_paper":"/paper/2405.14200","citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:af6259192d2927e02c6c9b40d6aee6712bf0b11f355d4ab7b3527eff7e8cfb20","observation_id":"00cf5997-01ae-49b7-a459-851114d44681","resolution":{"observed_at":"2026-08-06T21:38:17.730988Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-06T21:38:27.541229Z","title":"A survey on visual mamba,","venue":null,"work_id":"8bb31419-b722-45bf-a15f-818eefcc6893","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:12.112344Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:96cb2b13c42b568ce8f5be503b8dd3f35078e83a7e0c99d1a119a3921cd5113b","observation_id":"82982036-3872-46d8-a024-d3183cc5c650","resolution":{"observed_at":"2026-08-06T21:38:27.673945Z","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":"2026-08-06T21:38:27.280546Z","title":"Prompt learning in computer vision: a survey,","venue":null,"work_id":"d2f1e99a-b3a5-418a-9417-fbf01947c41e","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:12.192795Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:946de2f5d2b4a21a760128e343fa26c84219ee60b5d538565114566b1b12fa5d","observation_id":"225d16ac-2b7b-4705-9218-edeb165d6f2b","resolution":{"observed_at":"2026-08-06T21:38:27.438429Z","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":"2404.09516","last_updated":"2024-04-15T07:24:45Z","snapshot_observed_at":"2026-08-10T03:43:24.928430Z","submitted_at":"2024-04-15T07:24:45Z","title":"State Space Model for New-Generation Network Alternative to Transformers: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09516","snapshot_observed_at":"2026-08-06T21:38:12.271620Z","title":"State space model for new-generation network alternative to transformers: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:12.271620Z"},"links":{"cited_paper":"/paper/2404.09516","citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:f54924e14cbc38c99da392589a25d34de21b5651561687d4c70502596b386e5d","observation_id":"cc7f4305-d06c-4930-8f2f-109f3216c99f","resolution":{"observed_at":"2026-08-06T21:38:12.271620Z","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":"2026-08-06T21:38:27.118830Z","title":"Object tracking by jointly exploiting frame and event domain,","venue":null,"work_id":"094fb17a-61f3-4dbe-ba7e-9900bcbcac21","year":2021},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:12.375906Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:251fd588408bb74903b6a19a93ec6504da46ec15e0ca307fca7ac3760fac88f0","observation_id":"2dbb0a4f-806d-47b0-ac73-e1ac3d35b5bf","resolution":{"observed_at":"2026-08-06T21:38:27.170847Z","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":"2026-08-06T21:38:26.951286Z","title":"Visevent: Reliable object tracking via collaboration of frame and event flows,","venue":null,"work_id":"f4bd8c2c-d9bb-41f5-a25f-ec79fd4bcc71","year":1997},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:12.465331Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:1b09b529d52f5258acb42a2cf6b69059f5baeb53be32a4f27fa4a75e7a16aa3b","observation_id":"4ebaba5b-20a0-41b8-a029-059bebb3e03a","resolution":{"observed_at":"2026-08-06T21:38:27.049517Z","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":"2026-08-06T21:38:26.803135Z","title":"Tenet: targetness entanglement incorporating with multi-scale pooling and mutually-guided fusion for rgb-e object tracking,","venue":null,"work_id":"5b6889d4-5a92-4237-8b09-f285f24c4a64","year":2025},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:12.550593Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:bfb4fe1417c0adb5ca25a506ae07997588b59c7cf7aff2fb1e98c5b137b9e72e","observation_id":"9f92b9ba-2d08-4e66-8ab4-037aab329fef","resolution":{"observed_at":"2026-08-06T21:38:26.878795Z","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":"2026-08-06T21:38:26.618439Z","title":"Emtrack: Efficient multimodal object tracking,","venue":null,"work_id":"24519a96-f410-47ea-a733-9d05a54bee88","year":2025},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:12.645044Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:8dac65a8ba2b341acca9945577cbbd3fd6c5aaef1d0d65358e74030a1d365b25","observation_id":"233d3521-5de3-4408-aa00-487951ae606c","resolution":{"observed_at":"2026-08-06T21:38:26.731195Z","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":"2026-08-06T21:38:26.306914Z","title":"Sdstrack: Self-distillation symmetric adapter learning for multi-modal visual object tracking,","venue":null,"work_id":"68fa3858-d57b-48ce-97c4-173f6e1638ae","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:12.730801Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:459c434044be22fba08cd106e641cdfbf679b1d2399415d6562a842c46107f9c","observation_id":"0ce2a3db-535e-4ea6-bb97-30071213fed8","resolution":{"observed_at":"2026-08-06T21:38:26.468042Z","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":"2026-08-06T21:38:26.009039Z","title":"Exploiting multimodal spatial-temporal patterns for video object tracking,","venue":null,"work_id":"b7019c4d-1cb6-4461-80db-098368fbca4b","year":2025},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:12.818968Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:808080959b3e11d0d7c4b4930a66c0eb8ef95113f19093693ed272c0679d6492","observation_id":"6e67dcf5-40a1-4f94-9be5-972d8695083d","resolution":{"observed_at":"2026-08-06T21:38:26.136962Z","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":"2026-08-06T21:38:25.831131Z","title":"Onetracker: Unifying visual object tracking with foundation models and efficient tuning,","venue":null,"work_id":"7b0cd347-632c-4fcb-9df7-15be93f3e035","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:12.929491Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:388ee42a6cdd7180aade6370f54074c0114b9c9e6d9926efd3661dafc7274c79","observation_id":"9b82a5d1-1111-4cc5-8b9d-b2d2d4b239d3","resolution":{"observed_at":"2026-08-06T21:38:25.902436Z","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":"2026-08-06T21:38:25.565894Z","title":"Cross-modal orthogonal high-rank augmentation for rgb-event transformer-trackers,","venue":null,"work_id":"8c9d9525-438f-43d0-b281-c2ae67c996bd","year":2023},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:12.995563Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:477e8bf849db46d7948364366eddff63098476dacda72813994e2c3d387f15f3","observation_id":"3765512a-a99e-499e-9085-dbdf7f078205","resolution":{"observed_at":"2026-08-06T21:38:25.697014Z","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":"2026-08-06T21:38:25.361670Z","title":"Combining recurrent, convolutional, and continuous-time models with linear state space layers,","venue":null,"work_id":"1441b834-bf3e-4b79-be2f-82ae7368085a","year":2021},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:13.072079Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:5a2a673e06652b3e8cd0789bde4c592d916065669ee15b786a498655ebf99ba1","observation_id":"ce90967f-9c85-4513-a19f-e190d1e0e4b3","resolution":{"observed_at":"2026-08-06T21:38:25.458440Z","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":"2026-08-06T21:38:25.136961Z","title":"Hippo: Recurrent memory with optimal polynomial projections,","venue":null,"work_id":"03cf15f4-de2d-4649-80f4-f27f08e347b5","year":2020},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:13.159324Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:09468faf1e6438de341d4101db058a686c0c37769dcad8d5e9b545b16730a978","observation_id":"5932a336-8b46-45bd-898f-17a6912b5ca4","resolution":{"observed_at":"2026-08-06T21:38:25.207805Z","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":"2026-08-06T21:38:24.962018Z","title":"Mamba: Linear-time sequence modeling with selective state spaces,","venue":null,"work_id":"78d12131-48f5-42bb-9684-dd1261a94c6d","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:13.252834Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:de0aaa0a50ad348cb0e6076f6656350d66cefd38d7e8a2639f727e1672d8176e","observation_id":"5a4ac9d6-af31-4c39-becd-e111a6995065","resolution":{"observed_at":"2026-08-06T21:38:25.071005Z","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":"2026-08-06T21:38:24.777716Z","title":"Decision mamba: A multi-grained state space model with self-evolution regularization for offline rl,","venue":null,"work_id":"bef6f2df-3204-4c2c-af1f-e270d43eaeca","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:13.309938Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:43e28901d6a48e3a639688f0d28f649dd6cc1e377ca7b694c98d22575960dfa6","observation_id":"d82c9b0e-1b2d-4fe1-b8cb-af40af8a4754","resolution":{"observed_at":"2026-08-06T21:38:24.878599Z","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":"2404.04567","last_updated":"2024-04-06T09:30:38Z","snapshot_observed_at":"2026-07-06T17:56:27.818353Z","submitted_at":"2024-04-06T09:30:38Z","title":"Optimization of Lightweight Malware Detection Models For AIoT Devices","version":1},"cited_work":{"arxiv_id":"2404.04567","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.04567","snapshot_observed_at":"2026-08-06T21:38:17.383084Z","title":"Optimization of Lightweight Malware Detection Models For AIoT Devices","venue":"cs.CR","work_id":"dee36d04-a783-4b53-9b1e-88c44a4be183","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:13.382736Z"},"links":{"cited_paper":"/paper/2404.04567","citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:365653cf302231a25673b0c477e3525f038a3df5df00a4288138f34a87169b48","observation_id":"c129ef44-b04e-4e15-8872-ee8e9833edb3","resolution":{"observed_at":"2026-08-06T21:38:17.462267Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-06T21:38:24.581977Z","title":"Fusionmamba: Dynamic feature enhancement for multimodal image fusion with mamba,","venue":null,"work_id":"e17dec27-b03f-4592-ae50-ee025305c304","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:13.468947Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:d77878517f92ad221349360f8e4b668abf0695cbe15f61a64d63bfb7298ca5ef","observation_id":"58ef898c-e68d-4530-8acd-46afb41ba3d2","resolution":{"observed_at":"2026-08-06T21:38:24.671785Z","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":"2026-08-06T21:38:24.365727Z","title":"Visual prompt tuning,","venue":null,"work_id":"c664234e-01c4-4b78-b8b9-941456bd6c67","year":2022},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:13.539612Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:8ebf5b75af648478532ae4de6f1ca47651e0cc369b6ff49d27074097cbfaaf31","observation_id":"de21824d-4e72-43d7-952f-25c12d269f10","resolution":{"observed_at":"2026-08-06T21:38:24.465581Z","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":"2026-08-06T21:38:13.625977Z","title":"Learning to prompt for vision- language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:13.625977Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:6bbc68077663400b5c2e9943a901d25ed2233d9abdbb90334e2f16f09c5f01f9","observation_id":"16efdcd4-f000-455e-855e-a9bd8fe48bc4","resolution":{"observed_at":"2026-08-06T21:38:13.625977Z","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":"2026-08-06T21:38:13.724104Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:13.724104Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:6506f08e0452a1a5c7ea7032f923263fd64756104aa3c5bd260e18456ad2f998","observation_id":"3089653b-9535-4230-95a5-3415fde691d6","resolution":{"observed_at":"2026-08-06T21:38:13.724104Z","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":"2026-08-06T21:38:24.137132Z","title":"Snnptrack: Spiking neural network based prompt for high-accuracy rgbe tracking,","venue":null,"work_id":"b2013587-872f-4d3b-9cc2-42ac7b6cd845","year":2025},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:13.808333Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:dae21d3e12ed2df69f6066f184195bad105f24a16854a018721d5e5a4f9870bb","observation_id":"170fbdbf-9cb9-4459-8856-0e402a23eabe","resolution":{"observed_at":"2026-08-06T21:38:24.265215Z","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":"2026-08-06T21:38:23.929152Z","title":"Visual prompt multi- modal tracking,","venue":null,"work_id":"931d2414-0398-4563-ad0a-5e84c9350b4d","year":2023},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:13.903901Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:70241f81dff7db5b50880de6ff9fd57ed6a64bc9aba279923adc51fc01ec3422","observation_id":"002e8db0-f4d2-4acc-8e92-494ce8f02c6a","resolution":{"observed_at":"2026-08-06T21:38:24.001022Z","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":"2026-08-06T21:38:23.774254Z","title":"Temporal adaptive rgbt tracking with modality prompt,","venue":null,"work_id":"2190b35e-09ac-44f7-9540-2fc94debd4a5","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:13.983595Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:beea0a929dec7223557382deb2f2bea72df1ae7c5c6c6a5c7742edc6d3ce833e","observation_id":"cdf9bb71-37c3-4e3c-8c73-f75578a13ccf","resolution":{"observed_at":"2026-08-06T21:38:23.857780Z","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":"2026-08-06T21:38:23.610405Z","title":"Explicit visual prompts for visual object tracking,","venue":null,"work_id":"fd5a5edd-8f97-4e2e-84a3-6fb1cd9c2723","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.087355Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:86dad5ce0a7ffe52fe221c19a40f53f60de314deca1ee6f03b4fb9ddaa470029","observation_id":"c5317efe-cdd7-4e19-b3b2-eaddb82412f4","resolution":{"observed_at":"2026-08-06T21:38:23.697406Z","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":"2026-08-06T21:38:23.434564Z","title":"Joint feature learning and relation modeling for tracking: A one-stream framework,","venue":null,"work_id":"d7010e69-169e-4ece-b529-38295015b836","year":2022},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.153687Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:a56ec59eb9a8ee73becb1acd6244180dc8cc4e223e2c9d49262a504a92e65adb","observation_id":"7cb45031-9499-46cf-8951-88cceade043d","resolution":{"observed_at":"2026-08-06T21:38:23.507629Z","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":"2026-08-06T21:38:23.258422Z","title":"Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models,","venue":null,"work_id":"371503c1-b3f9-44d6-bb94-f77746b5a061","year":2017},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.217952Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:e2979688e7cbbce0fcf8ddddda7529a21f4fce0813b4284ffc40f466e1b15813","observation_id":"65020d06-3d50-4add-a008-d1ed1719ea9d","resolution":{"observed_at":"2026-08-06T21:38:23.359196Z","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":"2026-08-06T21:38:23.094027Z","title":"Decoupled weight decay regularization,","venue":null,"work_id":"76492d35-1956-4f9f-8c23-28adc3186d97","year":2018},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.282544Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:5d6ee538d4c2322b269fa6fcb0a0d004c8b2d23b1ee0a2f4f29e20b68d36ec7c","observation_id":"aa1d294d-e37c-4bf3-8fad-0de87fed013a","resolution":{"observed_at":"2026-08-06T21:38:23.160991Z","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":"2026-08-06T21:38:14.362194Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.362194Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:a734fb9e6d739716c1520e25ae794c497c913280c7ffc70f137b2f5333466090","observation_id":"0f2ea592-bc0e-49d8-85b1-a49ca1397832","resolution":{"observed_at":"2026-08-06T21:38:14.362194Z","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":"2026-08-06T21:38:22.826730Z","title":"High performance visual tracking with siamese region proposal network,","venue":null,"work_id":"ce7e34ca-f1d9-4a94-b9b8-83317b82f648","year":2018},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.431323Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:9f29684e684fc5a4dd7147c23648c0c8a2b28518d0219a63d2d446d8d566eff0","observation_id":"7dc073b1-544b-4dca-9282-78cabd9f3728","resolution":{"observed_at":"2026-08-06T21:38:22.981125Z","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":"2026-08-06T21:38:22.416138Z","title":"Siamfc++: Towards robust and accurate visual tracking with target estimation guidelines,","venue":null,"work_id":"aeb77464-d43a-4472-a4d4-706dc1edeee0","year":2020},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.518955Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:b5573adb55c167d9ce9f40225314a9e61378cdf8b9c385a9fbcead77e2ed2c13","observation_id":"c9df768c-9fa2-4e71-a804-80e3a0c5cb62","resolution":{"observed_at":"2026-08-06T21:38:22.607720Z","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":"2026-08-06T21:38:22.033454Z","title":"Know your surroundings: Exploiting scene information for object tracking,","venue":null,"work_id":"f51a29b4-4835-4424-860b-b3936799ab2d","year":2020},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.577622Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:4359aa56ca2e19d9a93a9341fb9e42d789b2ba7e36d242a228e75f721a0a1abb","observation_id":"65e864bc-d9a1-45e4-91e3-eca244b95c8a","resolution":{"observed_at":"2026-08-06T21:38:22.246182Z","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":"2026-08-06T21:38:21.817124Z","title":"Clnet: A compact latent network for fast adjusting siamese trackers,","venue":null,"work_id":"2c87053c-234c-4376-94c6-53614e5ce36b","year":2020},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.639004Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:d1ec55bd9150ea9d74b74368511bde5a5e834a33c2f4157fb522d94771211b0e","observation_id":"074d884d-ba90-46a5-a2a0-403df0552746","resolution":{"observed_at":"2026-08-06T21:38:21.895869Z","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":"2026-08-06T21:38:21.633458Z","title":"Atom: Accurate tracking by overlap maximization,","venue":null,"work_id":"68955da1-a745-4523-be60-546abb808d7c","year":2019},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.643160Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:d34040ad1b58904bf1147bde6d979af8d148bfdc9d6ef0ba0b80accdefdcdb9c","observation_id":"22ea3e29-ba4c-4bd4-9225-88435aa14cb3","resolution":{"observed_at":"2026-08-06T21:38:21.726870Z","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":"2026-08-06T21:38:21.464959Z","title":"Learning discrim- inative model prediction for tracking,","venue":null,"work_id":"fabbab04-c856-4d11-bcbc-5c941be6c54c","year":2019},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.760488Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:b3229eae5fe2b9a2bf115d56cfea6089434156dd8f36f5cb8a4d862244684def","observation_id":"8b9aa019-8898-45b3-b12a-80f1e1cd7719","resolution":{"observed_at":"2026-08-06T21:38:21.542941Z","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":"2026-08-06T21:38:21.277422Z","title":"Probabilistic regression for visual tracking,","venue":null,"work_id":"6a98fdf8-8ff8-4d9d-8383-9e9cb9dcb06c","year":2020},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.845332Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:843bc2fb8e64ba8c07e5b3d16976d95e5b6eac25c16faa028dfe7d132063e7c0","observation_id":"0bce6d1e-eaee-46a9-b780-98280454a72b","resolution":{"observed_at":"2026-08-06T21:38:21.373825Z","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":"2026-08-06T21:38:21.108655Z","title":"Mixformer: End-to-end tracking with iterative mixed attention,","venue":null,"work_id":"2fe8b073-fee5-4654-a4e7-559612633142","year":2022},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.988925Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:c485c737c3cacf9b874feee3b70011aa48db51ba8be1aabf47682b7b6596d2d6","observation_id":"fd97d4a2-87af-49c3-b6d6-16872abe89fc","resolution":{"observed_at":"2026-08-06T21:38:21.179798Z","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":"2026-08-06T21:38:20.985435Z","title":"Backbone is all your need: A simplified architecture for visual object tracking,","venue":null,"work_id":"b2ed6ac1-6edb-4575-b4ed-53a46dd534d7","year":2022},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:15.094176Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:d2fe9dc410b22174cd64463afdc715f5a35fb077e87e8e9ffef0f6f22133920a","observation_id":"1b6f0fd5-bd18-4a87-baa0-98b5bd82310b","resolution":{"observed_at":"2026-08-06T21:38:21.036932Z","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":"2026-08-06T21:38:20.843276Z","title":"Generalized relation modeling for transformer tracking,","venue":null,"work_id":"61a1c8af-bbb8-4f84-8cd4-953f1562f15d","year":2023},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:15.212669Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:906d7f38fe361cbc6b9a17f8d09ff1dc4c1fd0f49419099197f284aa818a94fe","observation_id":"f4ae3662-56fb-4611-8d7c-4cce58ecd920","resolution":{"observed_at":"2026-08-06T21:38:20.915385Z","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":"2026-08-06T21:38:20.685112Z","title":"Robust object modeling for visual tracking,","venue":null,"work_id":"293b30c0-8ce1-4590-952b-de63cdc91a12","year":2023},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:15.320965Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:ca28621954237462e89b4faf7c95cbe8dc7ad64e99a5e35cbb72e8ffe9f722c6","observation_id":"56f058c3-6303-4fd7-abe3-19d70a7c8710","resolution":{"observed_at":"2026-08-06T21:38:20.745840Z","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":"2026-08-06T21:38:20.557220Z","title":"Seqtrack: Sequence to sequence learning for visual object tracking,","venue":null,"work_id":"40bb87cc-3a28-49e9-880e-1d54b58b7cd1","year":2023},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:15.408155Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:249c03b4da9ea201dffe444fe90734b6b7ab0bfb0ecd07532afd4decfa9321ee","observation_id":"232947d7-bd59-4b9c-9ba3-42e56e7538b2","resolution":{"observed_at":"2026-08-06T21:38:20.622643Z","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":"2026-08-06T21:38:20.431802Z","title":"Hiptrack: Visual tracking with historical prompts,","venue":null,"work_id":"508a4de9-c845-45b6-b5a5-8d172553e798","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:15.482347Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:66336f8c4b0097e76d5c729e43307b5ab307d5dc689511820bd82fcf8eedc85b","observation_id":"b029241e-358e-4ebd-b527-0c726d6fa0c3","resolution":{"observed_at":"2026-08-06T21:38:20.496251Z","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":"2026-08-06T21:38:20.288309Z","title":"Odtrack: Online dense temporal token learning for visual tracking,","venue":null,"work_id":"daee1b11-2b40-4a74-b737-47fd2632dd55","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:15.598777Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:90bdd66d3104191c810d1b1746406e049e9653e01d5c2c41f27ae50d428dcaca","observation_id":"703aea08-71f6-4f17-b6f8-c77a0ec5980e","resolution":{"observed_at":"2026-08-06T21:38:20.362491Z","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":"2026-08-06T21:38:20.163779Z","title":"Single-model and any-modality for video object tracking,","venue":null,"work_id":"eaa1e1a4-7bb4-47ae-8d75-0255af35d99e","year":2024},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:15.795659Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:cf4152e9d16e3d3dc5984a88471f6e0c0066e91e75fe53239ce21ae050a7c59a","observation_id":"4cd0e9e6-e7fb-4f36-8d14-b93c2bb989a9","resolution":{"observed_at":"2026-08-06T21:38:20.215811Z","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":"2026-08-06T21:38:19.939568Z","title":"Less is more: Token context-aware learning for object tracking,","venue":null,"work_id":"175612d8-c5e5-4476-8ea1-04b92120d885","year":2025},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:15.949767Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:a795db110e628fe46b7b6887b572607a86a3cf5498421d0ceb74707e8e2d3edb","observation_id":"161eb4ed-6716-4634-9a5b-c11e9e71dc5e","resolution":{"observed_at":"2026-08-06T21:38:20.026887Z","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":"2026-08-06T21:38:19.676891Z","title":"Two- stream beats one-stream: asymmetric siamese network for efficient visual tracking,","venue":null,"work_id":"0b03e907-9b6c-45e7-b5b6-64286386eece","year":2025},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:16.042116Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:e731d740a425e99e36dc1f37a2b30a933fd975a101557ff5ba64a56a97fc670f","observation_id":"470e50a0-e7d0-43c1-818f-af40a597d5c2","resolution":{"observed_at":"2026-08-06T21:38:19.782447Z","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":"2026-08-06T21:38:19.368378Z","title":"Transformer tracking,","venue":null,"work_id":"fd3bc6e2-0de2-433b-94b4-dde391a002f4","year":2021},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:16.168529Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:0a5f514f8ee872e8432f639adfc52244b07b3b54c9125c7667316fa0ca8a840a","observation_id":"9cf261c9-a18a-4d9f-8648-595a737eca84","resolution":{"observed_at":"2026-08-06T21:38:19.498731Z","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":"2026-08-06T21:38:19.187673Z","title":"Learning spatio- temporal transformer for visual tracking,","venue":null,"work_id":"f1fef543-ef50-4d24-badd-8d0c4671f5a2","year":2021},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:16.279297Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:7f71040aae5e4568a379d5ad3faf224c14ab57d6277fac5eae920f7ddbfcb5f7","observation_id":"6a23f756-035c-4bbc-9482-35385ef29e90","resolution":{"observed_at":"2026-08-06T21:38:19.304022Z","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":"2026-08-06T21:38:16.430463Z","title":"Siam r-cnn: Visual tracking by re-detection,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:16.430463Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:c9276a36d2fdab4577df1e4f1d59386b9c87d64cb5b0ad2f07d605c6a5ab59ca","observation_id":"8fb09fa0-d635-47d2-865d-db44d3502597","resolution":{"observed_at":"2026-08-06T21:38:16.430463Z","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":"2026-08-06T21:38:18.908560Z","title":"Transforming model prediction for tracking,","venue":null,"work_id":"a4d094fa-6d5e-48fe-bce2-bbed8b9c3223","year":2022},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:16.542604Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:02ce72ab4bc1be43ebe23a58dfbbb705dffecc207d0e06a7dd65520dd50e621b","observation_id":"7367bca0-e3a4-4ff8-b011-d234deb9bb78","resolution":{"observed_at":"2026-08-06T21:38:19.084487Z","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":"2026-08-06T21:38:18.612554Z","title":"Learning target candidate association to keep track of what not to track,","venue":null,"work_id":"22ffb22d-69fd-4326-ad08-697e421bb089","year":2021},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:16.646170Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:351fbba81f89c62d613668f0d2e4d80da10c4fc8096fc4c05ac9c6cd05cef28e","observation_id":"eac00273-1896-4d4b-bded-db540150654f","resolution":{"observed_at":"2026-08-06T21:38:18.764995Z","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":"2026-08-06T21:38:18.385761Z","title":"Probabilistic regression for visual tracking,","venue":null,"work_id":"ac8c5fa5-6298-4591-9f98-e91d45dca1b2","year":2020},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:16.792159Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:c2c95938ea8c9cb4f6d8abe3a6c56b2357f33e7f4e4e1835656f9c89a397044f","observation_id":"2318c920-5360-489d-a753-2fa6a2299317","resolution":{"observed_at":"2026-08-06T21:38:18.476838Z","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":"2026-08-06T21:38:18.142031Z","title":"Transformer meets tracker: Exploiting temporal context for robust visual tracking,","venue":null,"work_id":"4bfca55e-94ce-49ae-84c3-09f8ce2f8db6","year":2021},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:16.983544Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:705cf02d2354539a22513775548203ef41a29848b5bb25be0d3ed6cefa5466f9","observation_id":"6b726bca-07e8-4e5a-86d2-334b471a9384","resolution":{"observed_at":"2026-08-06T21:38:18.302558Z","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":"2026-08-06T21:38:17.943648Z","title":"Cross-modality distilla- tion for multi-modal tracking,","venue":null,"work_id":"7f50a136-68c4-4ab0-94a4-50bd449a680d","year":2025},"citing_paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:17.192543Z"},"links":{"citing_paper":"/paper/2506.23783"},"observation_digest":"sha256:7439f434174ebac16548bd700d6e7c194d70e12f9fc9e9cd1ad94837b06f7832","observation_id":"82369441-ca5b-4776-970d-4ddb88d7e1e8","resolution":{"observed_at":"2026-08-06T21:38:18.039221Z","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"}}],"paper":{"arxiv_id":"2506.23783","last_updated":"2025-06-30T12:24:01Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T18:31:38.116456Z","submitted_at":"2025-06-30T12:24:01Z","title":"Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":2,"verified_fuzzy":62},"total_outbound_references":75},"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 11 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 3 inbound Pith citation observations for arXiv:2506.23783."}