{"as_of":"2026-08-15T18:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a8ba398743128d70a24bac0dda5a749d0c3d3206c8b1c5d9b73da98f901b8d42","coverage":[{"denominator":66,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":66,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T21:00:49.659320Z","state":"measured"},{"denominator":68,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":68,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:52:42.802886Z","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":"2026-07-03T00:47:30.786810Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"cited_work":{"arxiv_id":"2509.08265","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.08265","snapshot_observed_at":"2026-07-03T00:47:30.786810Z","title":"Hyperspectral mamba for hyperspectral object tracking,","venue":null,"work_id":"1b29769a-710d-4249-b081-9fcbc151a4f7","year":2025},"citing_paper":{"arxiv_id":"2606.09167","last_updated":"2026-06-08T08:05:40Z","snapshot_observed_at":"2026-08-12T23:43:39.120516Z","submitted_at":"2026-06-08T08:05:40Z","title":"Vision-Language Guided Hyperspectral Object Tracking via Semantics Fusion and Contextual Template Updating","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-27T17:00:06.124148Z"},"links":{"cited_paper":"/paper/2509.08265","citing_paper":"/paper/2606.09167"},"observation_digest":"sha256:9a29cdc915da4071b5ba0ba41c470c2a5a394cd60a070215f6b0f5cd9df2f804","observation_id":"a181e800-6f83-43d0-92b1-112c11ead941","resolution":{"observed_at":"2026-07-03T00:47:30.788486Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.08265","snapshot_observed_at":"2026-08-11T14:52:42.802886Z","title":"Hyperspectral mamba for hyperspectral object tracking,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.09575","last_updated":"2026-08-10T13:09:25Z","snapshot_observed_at":"2026-08-15T16:14:06.340979Z","submitted_at":"2026-08-10T13:09:25Z","title":"MSP-Net: Manifold-Guided Spectral Prompt Network for Hyperspectral Object Tracking","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T14:52:42.802886Z"},"links":{"cited_paper":"/paper/2509.08265","citing_paper":"/paper/2608.09575"},"observation_digest":"sha256:947d292380746a86b89cd2b37c7d3352140af6a30ba4180f32b2e5f4b6f612b4","observation_id":"b9f08c10-e982-4a8f-a641-10a61a4fdc5f","resolution":{"observed_at":"2026-08-11T14:52:42.802886Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2509.08265/citation-record","integrity":"/paper/2509.08265/integrity","json":"/paper/2509.08265/citation-record.json","paper":"/paper/2509.08265"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:00:49.354354Z","title":"Material based object tracking in hyperspectral videos,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.354354Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:4fff3d5273431fef3a2b8dbda23adc68aacd827abbfcb5cf273838c66a238e6e","observation_id":"743163e4-ec6d-4708-93a4-ffb74504d0dc","resolution":{"observed_at":"2026-08-04T21:00:49.354354Z","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":"2026-08-04T21:00:50.706857Z","title":"Mixformer: End-to-end tracking with iterative mixed attention,","venue":null,"work_id":"6c24b988-6a40-435a-8a33-1318b75b4e9f","year":2022},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.360300Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:066e30865d45214215f02d114e4186598bf38777328263a71913356878fb025b","observation_id":"9dfc207d-3aed-44e3-aabf-d1ea1ec2df39","resolution":{"observed_at":"2026-08-04T21:00:50.711494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.691053Z","title":"High- performance transformer tracking,","venue":null,"work_id":"84001f15-9161-4efd-afac-ecf3b1460c02","year":2022},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.364876Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:2ad6a06bbba8ce16df997e013366692116f65d4f32e0788ed3338c87a530e7a0","observation_id":"432c4bea-e8ee-48fd-b2b3-14a6d72358cc","resolution":{"observed_at":"2026-08-04T21:00:50.695681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.369711Z","title":"Material- guided multiview fusion network for hyperspectral object tracking,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.369711Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:56c2e03b6e391bfcb3f9386095549246c432c8b0128a3a3d55094897fca7bc42","observation_id":"8fb01945-b69e-45f1-a329-3633404a715c","resolution":{"observed_at":"2026-08-04T21:00:49.369711Z","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":"2026-08-04T21:00:50.664566Z","title":"Tftn: A transformer-based fusion tracking framework of hyperspectral and rgb,","venue":null,"work_id":"e0d8508f-d91b-473c-b7f7-4971ac31bc5e","year":2022},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.374242Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:b0474483c356e881da43da798a719839bdea49ede13aa6ba8d38cac1b73da31d","observation_id":"360fa57b-c0bc-49b7-ba7a-532d1c96fb1a","resolution":{"observed_at":"2026-08-04T21:00:50.669430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.650267Z","title":"Band regrouping and response-level fusion for end-to-end hyperspectral object tracking,","venue":null,"work_id":"10dd99e1-3384-4dfa-8bda-a614ccf6896f","year":2021},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.379046Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:d868fd1e9bec4c971b0da723739f0b654ed3442474070315bb8bce9c8106c56a","observation_id":"466096f7-e877-4ae2-83c0-7fc366f14f07","resolution":{"observed_at":"2026-08-04T21:00:50.654788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.635489Z","title":"Swintrack: A simple and strong baseline for transformer tracking,","venue":null,"work_id":"cb8b1a90-b017-4918-9052-1f2d3f74b5ac","year":2022},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.384176Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:ffdb241a46754b1d02612c3cdcde113a0d15c3431147435c7b54c7aaf3f7e061","observation_id":"f83a732f-5730-471f-b78d-70870d21ec64","resolution":{"observed_at":"2026-08-04T21:00:50.639771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.618856Z","title":"Spirit: Spectral awareness interaction network with dynamic template for hyperspectral object tracking,","venue":null,"work_id":"2cc164f3-05cb-4462-8047-251ea367f0c1","year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.389582Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:4df72223a14924a367053bddfdf2133b370afc6d69cfabd6b6066cfb4144b7cb","observation_id":"adcc76c0-7b17-48b3-9e94-a030ed524a1e","resolution":{"observed_at":"2026-08-04T21:00:50.623876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.602072Z","title":"Siambag: Band attention grouping- based siamese object tracking network for hyperspectral videos,","venue":null,"work_id":"4b63e5fa-f82a-427f-a198-02b7a9fa8cce","year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.394739Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:ba2effb6003deffd9ada0d96dd0a90e3c6309551da4bd914bf8070f7ff231aa5","observation_id":"ac4492a0-2253-4d24-aeed-2e03f48b2ce5","resolution":{"observed_at":"2026-08-04T21:00:50.607061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.586278Z","title":"Bs-siamrpn: Hyperspectral video tracking based on band selection and the siamese region proposal network,","venue":null,"work_id":"a89909ba-f8e7-4a5d-85db-9275f9027687","year":2022},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.400046Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:37b8c42641845d6021f42da41911ba97c1a991934f7325da5403c8d5b0713db4","observation_id":"d01aa7fc-7988-45d1-b404-fc6e779913e5","resolution":{"observed_at":"2026-08-04T21:00:50.591288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.569483Z","title":"Hy-tracker: A novel framework for enhancing efficiency and accuracy of object tracking in hyperspectral videos,","venue":null,"work_id":"ca4b65ff-ffc7-4ae0-8db9-fa825a219e9c","year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.405417Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:33e9278bc7fc2fb33e1c6044a0be72820ccccdcc43ab0514aeb5b7946142c8a4","observation_id":"87ffe23c-ab69-45ed-95ec-61c54df902f2","resolution":{"observed_at":"2026-08-04T21:00:50.574773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.553256Z","title":"A siamese network-based tracking framework for hyperspectral video,","venue":null,"work_id":"61658b74-5067-4506-b6d2-43bacbd291ed","year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.410223Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:41af3054e6711eade4352a53ed13a5779ff44f4364cb2d732e8a61dabd7f4d08","observation_id":"ae1a1a40-3cad-4d0e-9b1f-b867d9269151","resolution":{"observed_at":"2026-08-04T21:00:50.557742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.538149Z","title":"Cbff-net: A new framework for efficient and accurate hyperspectral object tracking,","venue":null,"work_id":"23515002-e965-423c-a1e0-98b15596d82f","year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.414575Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:a9ce4e774a40ef0739dd155c98bafd950a655357784d035a6daeaf743c65414c","observation_id":"7987b71b-a73c-4d4b-b2f3-63bbf8a61832","resolution":{"observed_at":"2026-08-04T21:00:50.543431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.418710Z","title":"Learning a deep ensemble network with band importance for hyperspectral object tracking,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.418710Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:0ee8fbef0acc8420b61b38d0df8c0272de10309aedc07688c00a0c8030d4e1a0","observation_id":"a39ed539-2204-40a3-bd7c-2ed8f73f0410","resolution":{"observed_at":"2026-08-04T21:00:49.418710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-04T21:00:49.422961Z","title":"Mamba: Linear-time sequence modeling with selective state spaces,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.422961Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:fa458ac8ad6f5e00429db7817f5ab807abc00ce1ca9526a6219f758799edcec1","observation_id":"50111bc0-1ad2-4cc8-a27a-9c448574ffb0","resolution":{"observed_at":"2026-08-04T21:00:49.422961Z","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":"2026-08-13T18:01:05.424620Z","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":"2026-08-15T16:12:56.244110Z","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":"sha256:201a4cffe706d4a909af326556f7e214b4bfe48d7ac80563d08a56aa409daba1","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:00:49.432768Z","title":"Exploring enhanced contextual information for video-level 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":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.432768Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:70058e3e14d14932a846f1594da85d48c9a2cdd2cee27f557b5470816d14e077","observation_id":"30abfd81-e802-4e6b-8c26-661eb39ececf","resolution":{"observed_at":"2026-08-04T21:00:49.432768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09417","last_updated":"2024-11-14T02:00:33Z","snapshot_observed_at":"2026-08-14T11:12:31.002605Z","submitted_at":"2024-01-17T18:56:18Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09417","snapshot_observed_at":"2026-08-04T21:00:49.437678Z","title":"Vision mamba: Efficient visual representation learning with bidirectional state space model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.437678Z"},"links":{"cited_paper":"/paper/2401.09417","citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:318e32e8d582df55ba064d8d3e7abf97195c9a005413806be27c18f543bcac6b","observation_id":"0d951ee8-8d75-4057-ace0-8f359e4c6679","resolution":{"observed_at":"2026-08-04T21:00:49.437678Z","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":"2026-08-04T21:00:49.442331Z","title":"Vmamba: Visual state space model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.442331Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:6fe4fda68280278adcf95223f19e79b395e7b9c01e9323e26ee0e679099da7c0","observation_id":"db2b3994-abc5-4b97-919f-93c7184131f6","resolution":{"observed_at":"2026-08-04T21:00:49.442331Z","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":"2026-08-04T21:00:50.490055Z","title":"The hyperspectral object tracking challenge (hot2023),","venue":null,"work_id":"575f4b30-dc3f-492e-82b0-4f6072c87208","year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.446861Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:62d3dfe6456cebb046aff927c0c4afe7756e8af4194451a83d3d163f9c165f2f","observation_id":"881ee65f-5cff-44ed-97fb-eabf43d4f8e0","resolution":{"observed_at":"2026-08-04T21:00:50.495174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.473157Z","title":"The hyperspectral object tracking challenge (hot2024),","venue":null,"work_id":"ec4b42b9-39ad-4982-a140-380f89bdad61","year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.451688Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:1a5fbb2d2b703a5aea1b1778e89d446832c98f6d1f5ecfaf0c3c222fa3811d70","observation_id":"97c414ba-950a-4522-93d0-8c6bdae0adad","resolution":{"observed_at":"2026-08-04T21:00:50.477868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.455773Z","title":"Fully-convolutional siamese networks for object tracking,","venue":null,"work_id":"dfef8739-9359-4cf9-ad7a-123fbdc624d3","year":2016},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.455788Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:47bc3950858f65d3a25efefd3ed20b843eac8f1a81f74072c766e985ae3e8713","observation_id":"e9d9225f-939f-461d-8dbe-54eeb812b970","resolution":{"observed_at":"2026-08-04T21:00:50.460595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.434758Z","title":"High performance visual tracking with siamese region proposal network,","venue":null,"work_id":"0c3e6ea0-7b7c-40d6-8119-f3cddbb7915c","year":2018},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.460563Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:37f393fdac874ed9dafd3cf50924cdce38ed302d61886e6416088d5bb6cee347","observation_id":"52d0caa4-eae6-4da3-9b63-03a62ea050b7","resolution":{"observed_at":"2026-08-04T21:00:50.440461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.417005Z","title":"Siamrpn++: Evolution of siamese visual tracking with very deep networks,","venue":null,"work_id":"1f8d9f5c-30b1-4d09-8b0a-d55626e293cb","year":2019},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.465190Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:e2c2da00f9308f6ebc94a205611264f5e5c5a0daaac9b71a0e84dc4e4f45c6f2","observation_id":"7d8ec05f-e467-4cf4-b809-27a61cfca65c","resolution":{"observed_at":"2026-08-04T21:00:50.422594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.381360Z","title":"Deformable siamese attention networks for visual object tracking,","venue":null,"work_id":"866ea83e-27b3-49de-99b1-d6bc13d684dd","year":2020},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.474961Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:d517870863db4d997eff666edd10eea33dcf0eb9f3525fcd05f1bbb034cd990d","observation_id":"0c9637af-25d1-486e-8bb4-49dc3e6e053f","resolution":{"observed_at":"2026-08-04T21:00:50.386117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.366391Z","title":"Transformer tracking,","venue":null,"work_id":"74c26515-6ab4-4e8b-ab3a-2806352e876d","year":2021},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.479391Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:a4eefcaf5f22ed2752246aff07ce44df3d47337fbd96934ab907fe2e0df156e1","observation_id":"45647dbb-9c86-43c3-b232-b7435002c0ac","resolution":{"observed_at":"2026-08-04T21:00:50.371205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.349388Z","title":"Learning spatio-temporal transformer for visual tracking,","venue":null,"work_id":"4a0536c5-82da-46dc-8f0d-21a2ca33cb0a","year":2021},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.484326Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:8562f1e522c4a596e19c126e30cbe1a956d65e592f1a9714f541398159a368fb","observation_id":"5a182f0e-c559-4c94-a30a-5ceacc3c6038","resolution":{"observed_at":"2026-08-04T21:00:50.355103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.333788Z","title":"Seqtrack: Sequence to sequence learning for visual object tracking,","venue":null,"work_id":"a4a98332-1b79-4f15-8d00-38d609dcd0be","year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.488734Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:92860145ed88e77517318b443f347e8e7853f20739fb83b556c299bc69d683f3","observation_id":"9d823214-3e88-4138-a229-ab15c004a1af","resolution":{"observed_at":"2026-08-04T21:00:50.338697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.493187Z","title":"Autoregressive visual tracking,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.493187Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:44474f7cb021483de31c9bb1cfe9ceada7a76a352173ca02205df1c70b429207","observation_id":"1033fcd7-c300-4549-9908-b2b5188c5198","resolution":{"observed_at":"2026-08-04T21:00:49.493187Z","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":"2026-08-04T21:00:50.307555Z","title":"Hiptrack: Visual tracking with historical prompts,","venue":null,"work_id":"4e0d64b5-e0ee-44ce-8c54-38be77025eff","year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.498362Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:603836426b1b30f8261ad9252ebca172b67b38de087a53e8206e48ddbd55223c","observation_id":"59487c88-2132-4a48-8767-bb852f4377a5","resolution":{"observed_at":"2026-08-04T21:00:50.312103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.291745Z","title":"Tracking via object reflectance using a hyperspectral video camera,","venue":null,"work_id":"de2445f1-b4d7-4c81-bd1d-b779a4e81103","year":2010},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.502818Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:f440555a6bdde98e4dbc28ad982da61c4a0bf6d34f2afbf35e273e68fa7676c5","observation_id":"0594e6c0-bcbd-4f6e-a3fa-143d9e12789c","resolution":{"observed_at":"2026-08-04T21:00:50.296450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.507653Z","title":"Spatial–spectral weighted and regularized tensor sparse correlation filter for object tracking in hyper- spectral videos,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.507653Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:96d57846e5dfbc8832ac5ac7e9fa990baf875ff545e789ad9a004cabd53a7416","observation_id":"8ceb0955-352d-4974-b8cf-345eede1bc66","resolution":{"observed_at":"2026-08-04T21:00:49.507653Z","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":"2026-08-04T21:00:50.262272Z","title":"Siamohot: A lightweight dual siamese network for onboard hyperspectral object tracking via joint spatial-spectral knowledge distillation,","venue":null,"work_id":"19445925-c561-42ce-90f8-73b0026be8af","year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.512250Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:3aa019e311818d1b3d3fa67d7c480f41061f4e5fe3038bcd70d05fb313a8102b","observation_id":"b6882cac-8f32-4fe2-96a9-aec16b3bab25","resolution":{"observed_at":"2026-08-04T21:00:50.268034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.245030Z","title":"Spectral- spatial-aware transformer fusion network for hyperspectral object track- ing,","venue":null,"work_id":"8cffb9d7-8936-484b-8899-feef31f88603","year":2022},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.516925Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:ef24f976160847641a1d7d003774c2b7491ea12205d24fd8b137126eefca58e3","observation_id":"70e98b2a-968f-4aae-a5df-f39f79a6e115","resolution":{"observed_at":"2026-08-04T21:00:50.250927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.227294Z","title":"Spatial–spectral cross- correlation embedded dual-transfer network for object tracking using hyperspectral videos,","venue":null,"work_id":"48b74a15-af45-4b01-b9c0-9dc15eb36da3","year":2022},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.521764Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:738c4bdfb5bcb9b970e546606ad7a583aaa85104c89bf626b0d7ffe3bfb54f57","observation_id":"a1cf3b63-d3bb-4267-b776-9b8c0ce5827d","resolution":{"observed_at":"2026-08-04T21:00:50.233447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.209614Z","title":"Bae-net: A band attention aware ensemble network for hyperspectral object tracking,","venue":null,"work_id":"b41df9ea-88e7-4521-a653-2cf1e6d1caf2","year":2020},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.525916Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:47c0399bebfcec5f228e2f255c71709b019d7c2077a794efa0a4df12d29095c7","observation_id":"0a0a5e70-7ea0-429f-a7f1-8c2ac25ee296","resolution":{"observed_at":"2026-08-04T21:00:50.215889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.189326Z","title":"A spectral–spatial transformer fu- sion method for hyperspectral video tracking,","venue":null,"work_id":"810939cd-b073-4d6b-846e-baca453a84ad","year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.530218Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:34d8b0da3886777544b0f3a0155606bf8a75e94c803143425298f71e116da6c9","observation_id":"308974a7-d715-4b70-8893-65a99d227b5f","resolution":{"observed_at":"2026-08-04T21:00:50.195170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.172318Z","title":"Rgbt tracking via all-layer multimodal interactions with progressive fusion mamba,","venue":null,"work_id":"d27414de-e6ac-47ec-8dfe-b73ab909248f","year":2025},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.534649Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:e2b94ca524ea680442324644b08629e58e2ae2abf0393eb9a4cef0dbe581660f","observation_id":"ada8799c-daff-4f02-80a8-fe856e05efc4","resolution":{"observed_at":"2026-08-04T21:00:50.177277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.156528Z","title":"Smamba: Sparse mamba for event-based object detection,","venue":null,"work_id":"55c0687d-e896-48e1-8ae6-55fa5d8d522f","year":2025},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.539430Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:7c20dfb88ee8de90f5e88ebce64f8fd768db01f2ab52b8a9252162ee26de62e9","observation_id":"4a397610-7058-4a0f-8770-420928387995","resolution":{"observed_at":"2026-08-04T21:00:50.161482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.140401Z","title":"Trackmamba: Mamba-transformer tracking,","venue":null,"work_id":"d02d4b20-4264-435a-b23d-58821068964d","year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.545248Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:2c1ce1aa6030d6eafcd8141dc09d779c047543b5b29ee1de8700f1a2a147cb5c","observation_id":"84c6c842-ea01-49ff-8c55-dce8ff6254c1","resolution":{"observed_at":"2026-08-04T21:00:50.145232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.123596Z","title":"Mambaevt: Event stream based visual object tracking using state space model,","venue":null,"work_id":"c50db36e-fe0a-4148-9319-32b68fa27908","year":2025},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.549414Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:c5aae838fdbaff3901f33393d4c447f83635d4afad9420dd8505fee3e89f9de4","observation_id":"efda357b-65a1-4874-96a9-24db1dc5f860","resolution":{"observed_at":"2026-08-04T21:00:50.128421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.553721Z","title":"Root mean square layer normalization,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.553721Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:dba75ad50272b600f04b52becd6c94efdc3a70662c2404bb4abe43955a357561","observation_id":"a2584e1e-a792-40bc-816e-23a43cc59eb2","resolution":{"observed_at":"2026-08-04T21:00:49.553721Z","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":"2026-08-04T21:00:50.094853Z","title":"Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,","venue":null,"work_id":"cd557f48-028c-4118-a842-6c95811d5dae","year":2018},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.558961Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:1a881b2b1cda6fa1de6ce03cb257f98fc941bd1478810f1e25b0e74c4377032b","observation_id":"50750eba-016f-4b2e-b211-a3aa8125b913","resolution":{"observed_at":"2026-08-04T21:00:50.100338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.076504Z","title":"Su- track: Towards simple and unified single object tracking,","venue":null,"work_id":"7f43a721-63cb-4591-92c6-8778df3d232e","year":2025},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.563634Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:22709d3a3a84b7c9aae815edc927357bfbc9551980f8e8bc44c6941cd22123d9","observation_id":"373f7718-9710-4721-987e-33d8ce0796f1","resolution":{"observed_at":"2026-08-04T21:00:50.081808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.059764Z","title":"Cornernet: Detecting objects as paired keypoints,","venue":null,"work_id":"e2b612aa-e15b-43ad-9194-b26456990c1b","year":2018},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.568592Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:db5519b08b5b1308ba382711e53c659662c56ac1f14709209c0165d1e14e54df","observation_id":"b4659065-fb0e-4c66-8c69-7e820612775f","resolution":{"observed_at":"2026-08-04T21:00:50.065157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.573217Z","title":"Generalized intersection over union: A metric and a loss for bounding box regression,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.573217Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:e93a22dcbee328b55152244267013501825a4a377ef68fc7cbf1eece90483e99","observation_id":"6ba027fa-02c9-4c58-ab77-fe65c8bcd4a5","resolution":{"observed_at":"2026-08-04T21:00:49.573217Z","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":"2026-08-04T21:00:50.031399Z","title":"Hivit: A simpler and more efficient design of hierarchical vision transformer,","venue":null,"work_id":"1770508f-67f8-4083-b10a-81a47cab9d1a","year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.577377Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:43a9771ce85e5e0e08aa5ffa0a3641fda3c14d0dc5d704ff03a854be4e7981c6","observation_id":"e5aca011-1f0d-4490-ad61-0073524e6030","resolution":{"observed_at":"2026-08-04T21:00:50.035872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.399639Z","title":"Siamfc++: Towards robust and accurate visual tracking with target estimation guidelines,","venue":null,"work_id":"fbe8a407-0373-46ff-b981-1e427a8c92ed","year":2020},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.581656Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:88fc8df258291638a21b16ab7952aa64dd91b828e1fb557602ed13c58c539362","observation_id":"89d3317f-4571-4e10-81fa-6d3ad03b281e","resolution":{"observed_at":"2026-08-04T21:00:50.404530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:50.015135Z","title":"Siamcar: Siamese fully convolutional classification and regression for visual tracking,","venue":null,"work_id":"40eb52e2-0913-4fd2-9509-20c27d7657e7","year":2020},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.585758Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:d1be0724b0d3efe75059dbc096b47a7c1331699d7e79571b201b64d570c38dfe","observation_id":"44bf6f89-0ad7-4784-80f4-9ea18c1d3d8f","resolution":{"observed_at":"2026-08-04T21:00:50.020281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.997289Z","title":"Joint feature learning and relation modeling for tracking: A one-stream framework,","venue":null,"work_id":"b3e76c83-83fb-4f62-9e0f-dd46d931e6a2","year":2022},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.589918Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:804f7ac0bd8c8851973dda7a89e3bdc5ed35efe72dd11b27f548d6efa1a25ca0","observation_id":"77651180-5dec-450f-8fc6-3905985a5534","resolution":{"observed_at":"2026-08-04T21:00:50.002547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.594558Z","title":"Towards real- world visual tracking with temporal contexts,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.594558Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:079623d76607e7ac95bc5f36dcff77aa346da7b164678deb6847c3e834d9ccd2","observation_id":"97a96a34-47cb-4d60-b8fb-4da4b155e8c6","resolution":{"observed_at":"2026-08-04T21:00:49.594558Z","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":"2026-08-04T21:00:49.965101Z","title":"Separable self and mixed attention trans- formers for efficient object tracking,","venue":null,"work_id":"824da46e-729d-4553-996c-e43de8bd7e4d","year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.598991Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:269a7a1979fe7971d93b8b14aa16c5e9e3326a519748c7897c2525fa6d0c65c7","observation_id":"eba7e782-e321-41e0-9702-d51c5f00b02d","resolution":{"observed_at":"2026-08-04T21:00:49.971497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.603575Z","title":"Autoregressive queries for adaptive tracking with spatio-temporal trans- formers,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.603575Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:b9ffd4a2b95f47cad3b12be0eeafd6343d40671ec43dc5d7fdd5ac3e31394af6","observation_id":"2c1fc902-09d8-497f-aa4c-3d30e5f83bef","resolution":{"observed_at":"2026-08-04T21:00:49.603575Z","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":"2026-08-04T21:00:49.935769Z","title":"Artrackv2: Prompting autore- gressive tracker where to look and how to describe,","venue":null,"work_id":"0f6399be-9b22-4b58-af89-14359b8b6ed0","year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.607772Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:86f685b8888878a60bfb2fbfa731923073a6f2b419f2a6faceaf3fba7be29e8b","observation_id":"d5e607e8-4407-4072-9788-83e9c64069e0","resolution":{"observed_at":"2026-08-04T21:00:49.941110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.916837Z","title":"Promptvt: Prompting for efficient and accurate visual tracking,","venue":null,"work_id":"55a7fa1f-e079-4d50-887d-8971a242c141","year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.612524Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:13618757b9606ac4ea7e160c55d33bc1d5e82fa92b4bc5c3ff322ba13f9f1eeb","observation_id":"0688576c-c127-4ef1-a7bd-83c701000152","resolution":{"observed_at":"2026-08-04T21:00:49.922163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.898892Z","title":"Transformer- based band regrouping with feature refinement for hyperspectral object tracking,","venue":null,"work_id":"d1990610-dd7a-45b3-b6d3-22bf4a4cfdcd","year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.616569Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:3d6d9421f691a0f8256ef8a9b06e43b11f4300d76399affddb858ce160f6e043","observation_id":"c30078c8-7595-42a7-9f82-1d48f8b27acd","resolution":{"observed_at":"2026-08-04T21:00:49.904602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.880837Z","title":"Phtrack: Prompting for hyperspectral video tracking,","venue":null,"work_id":"06c856cd-75bf-4aab-85b5-e8aab931fe4a","year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.621040Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:4bfb4dee49d3aede885db0d431b8ef3d6c4a5010e6cdb336c54c875ebbe02e39","observation_id":"2ed60977-1833-442f-98b9-1aa330092d63","resolution":{"observed_at":"2026-08-04T21:00:49.887068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.864370Z","title":"Sense: Hyperspectral video object tracker via fusing material and motion cues,","venue":null,"work_id":"e097cfdf-bd84-437c-afe2-2f4ce4cd6e69","year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.625238Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:af7d7f1fe0d20f276750b9d21660fcd0aa0f0703530e630958c6c92ec516e424","observation_id":"c13ba19f-d579-43f4-bee3-fe45c3dc5eb9","resolution":{"observed_at":"2026-08-04T21:00:49.869027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.847704Z","title":"Hyperspectral video tracking with spectral-spatial fusion and memory enhancement,","venue":null,"work_id":"15f1f290-7793-440d-8222-e8f243773063","year":2025},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.629539Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:1e864bdf3f6979edb291a9808ec1e8e50000c2a66b0adf068417705391c1f943","observation_id":"8ee2f647-f138-4dfe-a124-e35b1dabce95","resolution":{"observed_at":"2026-08-04T21:00:49.853126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.633599Z","title":"Hyperspectral object tracking with spectral information prompt,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.633599Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:2b22e83d163bfd5f73ffe476d83dac65a36fbb4ee0c850a2237252f30b0454ea","observation_id":"944072dc-8c97-4c9a-8ac5-20fc29317a1b","resolution":{"observed_at":"2026-08-04T21:00:49.633599Z","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":"2026-08-04T21:00:49.820837Z","title":"Hotmoe: Exploring sparse mixture-of-experts for hyperspectral object tracking,","venue":null,"work_id":"620baae7-92fb-476c-8a62-0016284ec613","year":2025},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.638206Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:06ab5fb88f87406b132dde3404a8b15def3864961b0b90ffbbb4d0f48531282b","observation_id":"c4e6eb95-b07e-4fc8-8d8c-d21a49e62b19","resolution":{"observed_at":"2026-08-04T21:00:49.826275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.642424Z","title":"Multi-domain universal representation learning for hyperspectral 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":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.642424Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:f61a77b21ce3b771905c9912724664ddd3b6af7c7b68c098dc55e4fe5ba60261","observation_id":"4ea8ce01-f005-43dd-97f6-4849af0ba52a","resolution":{"observed_at":"2026-08-04T21:00:49.642424Z","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":"2026-08-04T21:00:49.792863Z","title":"Ssttrack: A unified hyperspectral video tracking framework via modeling spectral-spatial-temporal conditions,","venue":null,"work_id":"fabc78ee-5034-4b8a-a7c1-bff9faf8874a","year":2025},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.646531Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:71b1b62f546af1945a5d9a28cf8bc4bb2be31b38d1b05b192cf54bcc8284e937","observation_id":"338efc63-1703-4aa4-a44e-16174fbe4daa","resolution":{"observed_at":"2026-08-04T21:00:49.798162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.776778Z","title":"Domain adaptation- aware transformer for hyperspectral object tracking,","venue":null,"work_id":"d31430a4-3e3a-45f5-884c-ea73d34f7bcb","year":2024},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.650669Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:b3fa1cf52392a7113fd8e935b50b12a1105f3551b574cbdd5868e7563ea6e273","observation_id":"25e8676e-b434-4b38-9541-409fce137147","resolution":{"observed_at":"2026-08-04T21:00:49.781859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.759815Z","title":"Ubstrack: Unified band selection and multi-model ensemble for hyperspectral object tracking,","venue":null,"work_id":"ab1b8c18-30d7-49a7-877d-ff238a5be282","year":2025},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.654961Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:50f994f3665689fdcf9103da2096510b91e2feea9c492e83b203e48cbde364b5","observation_id":"f59a3e6f-1ded-4da0-b557-ecff473ba64c","resolution":{"observed_at":"2026-08-04T21:00:49.765523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-04T21:00:49.741695Z","title":"A transformer- based network for hyperspectral object tracking,","venue":null,"work_id":"e6b714ab-3108-4354-861c-e8fb4e4cb878","year":2023},"citing_paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-04T21:00:49.659320Z"},"links":{"citing_paper":"/paper/2509.08265"},"observation_digest":"sha256:438c7f3448dd36a1b277917a7f2510b4723bd867fdcbe23102d1e4e9a21fc0c7","observation_id":"39f6f676-1729-437d-a548-f5ea2046f2dd","resolution":{"observed_at":"2026-08-04T21:00:49.748355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.08265","last_updated":"2025-09-10T03:47:43Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-15T16:12:56.244110Z","submitted_at":"2025-09-10T03:47:43Z","title":"Hyperspectral Mamba for Hyperspectral Object Tracking"},"reference_resolution":{"displayed":66,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":0,"verified_fuzzy":50},"total_outbound_references":66},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 2 inbound Pith citation observations for arXiv:2509.08265."}