{"as_of":"2026-08-08T06:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:164c75e8d8c0e1f0d6314c22834f313839b7f54e549b304085f8b629317af9ca","coverage":[{"denominator":74,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":74,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T00:09:45.619182Z","state":"measured"},{"denominator":74,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":74,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.01488/citation-record","integrity":"/paper/2608.01488/integrity","json":"/paper/2608.01488/citation-record.json","paper":"/paper/2608.01488"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T00:09:46.449122Z","title":"Joint feature learning and relation modeling for tracking: A one-stream framework,","venue":null,"work_id":"049015c2-346a-4d69-8754-a4e32bc45553","year":2022},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.378498Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:bf89695afaf5f336d51d616b6054b85125004dc8c7cc4b34d502ef019b465b16","observation_id":"ed7c5e90-7d95-4826-b151-8d826f8327ff","resolution":{"observed_at":"2026-08-06T00:09:46.452760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.440420Z","title":"Hhtrack: Hyperspectral object tracking using hybrid attention,","venue":null,"work_id":"e073ffb6-1fa7-4b05-8416-de810babc571","year":2023},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.382756Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:87c3e8c483df754b9c8ef1e5f670a4a45e4edecace44caaa4cc6b8383ec07a9b","observation_id":"775cfa2d-a4d0-423f-8221-f1413f4af10f","resolution":{"observed_at":"2026-08-06T00:09:46.443744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.431814Z","title":"Mixformer: End-to-end tracking with iterative mixed attention,","venue":null,"work_id":"e998cebc-4559-436a-83d2-24933988e5cb","year":2022},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.385862Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:46fb5bed8148402bce3f271ae3f84dfbed471c72e609e5fb350713b0f12cfe27","observation_id":"87c2d92c-f04b-4c17-9362-e584e3a081e3","resolution":{"observed_at":"2026-08-06T00:09:46.434665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.421365Z","title":"Cafuser: Condition- aware multimodal fusion for robust semantic perception of driving scenes,","venue":null,"work_id":"33e694fa-590d-4551-be28-4913661ae87b","year":2025},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.389065Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:e3125494a0391da8ba5b83170cf6dfbda8516b6be0f870f3a69158f725e20e5e","observation_id":"5e16bc4a-d2f6-4058-a7b8-1f921940bd39","resolution":{"observed_at":"2026-08-06T00:09:46.425750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19138","last_updated":"2024-12-26T09:41:36Z","snapshot_observed_at":"2026-07-06T20:13:17.794908Z","submitted_at":"2024-12-26T09:41:36Z","title":"SUTrack: Towards Simple and Unified Single Object Tracking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19138","snapshot_observed_at":"2026-08-06T00:09:45.392791Z","title":"Sutrack: Towards simple and unified single object tracking,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.392791Z"},"links":{"cited_paper":"/paper/2412.19138","citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:bf45f06fec07b61c7816603a01d985311872f51052950f394b9533acee6c3ea1","observation_id":"e90ff0d1-0956-49c5-b8a4-68706949d38d","resolution":{"observed_at":"2026-08-06T00:09:45.392791Z","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":"2026-08-06T00:09:46.410794Z","title":"Single-model and any-modality for video object tracking,","venue":null,"work_id":"6b855eaa-ca8f-409f-afbf-feba11f6e4d1","year":2024},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.397068Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:4f99832a5112289ae6cf7e7c4aa12f5f8c85b7b52617dc250359f2359742593a","observation_id":"817ff638-167c-4c3c-99b5-15fe748bf163","resolution":{"observed_at":"2026-08-06T00:09:46.415340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.401678Z","title":"Sdstrack: Self-distillation symmetric adapter learning for multi-modal visual object tracking,","venue":null,"work_id":"a36d61b3-05ce-4f4e-bac5-339109b8b976","year":2024},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.401020Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:9a643d371311a8d9967dfc61c0802c8c6340d9ba5433b0d32967d88fd5a6f55f","observation_id":"446034d2-3dd7-4431-978c-a4626007ebe3","resolution":{"observed_at":"2026-08-06T00:09:46.405405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.392508Z","title":"Cross-modality distillation for multi-modal tracking,","venue":null,"work_id":"4c326d9e-826c-4ebe-b0de-2f6ffd428ac7","year":2025},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.404136Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:cb09df32d563d1c0a2e79ef4649613b0eab6533aff40b575f02187d170c27c20","observation_id":"d118faeb-1e07-4ae5-8151-0f675b966d4a","resolution":{"observed_at":"2026-08-06T00:09:46.396061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.383289Z","title":"Bi-directional adapter for multimodal tracking,","venue":null,"work_id":"018d74e7-4128-48d8-9a11-7e5dff8efc6b","year":2024},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.407190Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:dc0927b88ad82b97384a6b0dbb9515fe1fdd5075745263f31d5f2d5898f5beae","observation_id":"4b5320c6-bad2-49ce-9226-ab4053459c7a","resolution":{"observed_at":"2026-08-06T00:09:46.387135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.374628Z","title":"Visual prompt multi-modal tracking,","venue":null,"work_id":"4115f939-3ae6-44f4-8a34-4aa252f56ee0","year":2023},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.410544Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:a8e0e0ec7a13aaa67218802a43616dec4025cb2c5337d0da9f83df19fb7517de","observation_id":"d559e0fb-b9f3-4f9d-a453-34f3471397a4","resolution":{"observed_at":"2026-08-06T00:09:46.377836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.365626Z","title":"Onetracker: Unifying visual object tracking with foundation models and efficient tuning,","venue":null,"work_id":"44d9069b-1d8f-4da9-a0b9-0b2cb2e2a998","year":2024},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.414217Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:4ac24ed997a505262bdf093659e33e23f4b5a3a4147121d7829d2be86eacc979","observation_id":"70deafe0-b33f-4747-be2d-0c65987427b1","resolution":{"observed_at":"2026-08-06T00:09:46.369219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.346458Z","title":"Xtrack: Multimodal training boosts rgb-x video object trackers,","venue":null,"work_id":"49b582eb-93f1-4242-b973-86f397f7e5b7","year":2025},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.420278Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:17e5280f03ffbc9cf4fb354f6e52c0504cfd3c669792dbbe3f5366a8950be754","observation_id":"c9835702-4470-4eba-970f-7c77eb0880fe","resolution":{"observed_at":"2026-08-06T00:09:46.350256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.356224Z","title":"What you have is what you track: Adaptive and robust multimodal tracking,","venue":null,"work_id":"44d5aa2b-4022-4213-ad01-f07608f97bf2","year":2025},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.423086Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:710f4b54f6cfd45a549e5d77b8bc78427c8c6c013d9c4a79ef3c8eb491934a2d","observation_id":"8fa1deca-c807-4cb9-993f-b74e4e5d0ce3","resolution":{"observed_at":"2026-08-06T00:09:46.359847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15691","last_updated":"2024-12-20T09:10:17Z","snapshot_observed_at":"2026-07-06T20:10:46.746601Z","submitted_at":"2024-12-20T09:10:17Z","title":"Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15691","snapshot_observed_at":"2026-08-06T00:09:45.426152Z","title":"Exploiting multimodal spatial-temporal patterns for video object tracking,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.426152Z"},"links":{"cited_paper":"/paper/2412.15691","citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:597bccdb7331ac36380def497f5b096c2522b33bb4a3f4989e31de8bdb5b3f22","observation_id":"c929f761-7a88-45eb-be0e-0b4ac9742667","resolution":{"observed_at":"2026-08-06T00:09:45.426152Z","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":"2026-08-06T00:09:46.337899Z","title":"Mambaevt: Event stream-based visual object tracking using state space model,","venue":null,"work_id":"3dc7670a-c02e-45da-9928-42b641907241","year":2026},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.430147Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:7ac4bb562ca485e4623b1cdde40ccd3a8ad68543da316e35e4ccef981fe3abb0","observation_id":"dd66aa32-c1a9-40e3-a804-24cd734a3ba9","resolution":{"observed_at":"2026-08-06T00:09:46.341068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.329461Z","title":"Self-supervised learning for rgb-d object tracking,","venue":null,"work_id":"848ec5dd-cc9f-4039-a31f-e53016f94276","year":2024},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.433146Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:365b8b24435f1565313d4399d320beda53508904ac083c59e347fec6c10b03e5","observation_id":"7214ca39-0f0e-4d41-8a2f-6fb2d2ceaa9f","resolution":{"observed_at":"2026-08-06T00:09:46.332736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.320038Z","title":"Fmtrack: Frequency-aware interaction and multi-expert fusion for rgb- t tracking,","venue":null,"work_id":"447efa68-b619-45e4-b6c5-dacf35d5f743","year":2026},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.436203Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:d85ddaf54fc9e8864597f5e82ed2e94dc5b6396a93c3b12e1750f5132cf964a9","observation_id":"f5b8edf8-eeeb-476b-824a-85f45f0ba6b0","resolution":{"observed_at":"2026-08-06T00:09:46.323840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.310684Z","title":"Cross-modal orthogonal high-rank augmentation for rgb-event transformer-trackers,","venue":null,"work_id":"fa85dbfa-6658-46d0-9bd8-1bfe7e21f807","year":2023},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.439383Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:05d06c02941760edf8690087417965b7e7bc9f40d9aa70afeb9528464bd5a23b","observation_id":"ce8ad603-1c64-4021-9df9-bcc73348e676","resolution":{"observed_at":"2026-08-06T00:09:46.313734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.301540Z","title":"Lighttrack: Finding lightweight neural networks for object tracking via one-shot architecture search,","venue":null,"work_id":"d560937c-4103-4c6b-9b13-df5e7f662da2","year":2021},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.443361Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:6cfa8a3f9ae2a7ef4d9e3b37b253ecbffebc000d829217534e7c6682de164ea1","observation_id":"a9601beb-feff-43f4-867d-678869979b27","resolution":{"observed_at":"2026-08-06T00:09:46.304813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.293002Z","title":"Litetrack: Layer pruning with asynchronous feature extraction for lightweight and efficient visual tracking,","venue":null,"work_id":"b61f2ade-36b0-4638-9fb3-0d824f211e15","year":2024},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.446218Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:f000139e66bb5e5fb95c842bde1eb2c91fe0450a84f62de020dc7e51a07b4e69","observation_id":"009efb01-8016-4fa5-808d-5e09f57737cf","resolution":{"observed_at":"2026-08-06T00:09:46.296271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.284480Z","title":"Mixformerv2: Efficient fully transformer tracking,","venue":null,"work_id":"2d183c8f-60ff-4d5d-a600-471e9aa96b28","year":2023},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.449211Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:536af0a3884a56802a997c550209b3d4e96ae3c77b7f9bab93c77d28a9e99af8","observation_id":"76fa55c8-7166-43cc-a878-a8cd58c622e0","resolution":{"observed_at":"2026-08-06T00:09:46.287585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.275840Z","title":"Emtrack: Efficient multimodal object tracking,","venue":null,"work_id":"93a9abb8-90af-45ca-b21f-5b75bfef2d25","year":2025},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.452399Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:47e64defa801a0f498c32cdf448003249add0af0c38e15583caae3d1a9bd35bc","observation_id":"d24e43af-1b39-4176-b2e8-baab3af45435","resolution":{"observed_at":"2026-08-06T00:09:46.278871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.267222Z","title":"Exploring pruning-based efficient object tracking via hybrid knowledge distillation,","venue":null,"work_id":"d8acf66b-170f-462a-a65a-1b4953962eed","year":2026},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.455707Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:48de53a7f421e8e4c8cc46036976864e6c24629b68b89796434f493eac60db9d","observation_id":"5b718b61-3029-4be8-abe8-2e11c42da6a7","resolution":{"observed_at":"2026-08-06T00:09:46.270387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-06T00:09:45.458659Z","title":"Distilling the knowledge in a neural network,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.458659Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:7d5d4c5802c8f87af9258abecdc96e7d8de4751a6f9d4b272b0d442d6eea0bbe","observation_id":"6397810d-a8d0-4a47-a49c-23f481badfed","resolution":{"observed_at":"2026-08-06T00:09:45.458659Z","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":"2026-08-06T00:09:46.258291Z","title":"Decoupled knowledge distillation,","venue":null,"work_id":"ab644fbe-bef6-489f-948c-0a2af3b61f15","year":2022},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.462727Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:62540e68c7f39f576765fc329dd1bf8394130c600d51200300bd167d5b7cdc7b","observation_id":"60633b2c-997f-4567-85b3-ccda3a5951a2","resolution":{"observed_at":"2026-08-06T00:09:46.261525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.248625Z","title":"Relational knowledge distillation,","venue":null,"work_id":"5c9c31bc-a8a7-4401-a33b-fe57d0a7fc11","year":2019},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.466334Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:f9fc8e494916d50cc6af755e2006cba8de88620aea27c325ff2f8d288c7bfdb1","observation_id":"e5f6d67e-3063-470b-b361-e1ee35c6f21b","resolution":{"observed_at":"2026-08-06T00:09:46.252503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.238735Z","title":"Relational knowledge distillation,","venue":null,"work_id":"497d8c80-44ad-49c3-b4d4-c89a17d463c8","year":2019},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.470367Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:abd22a7efdd731ab07d1015149425850bec93470a90b1cf9f985c6b6472d3964","observation_id":"c62ca8dc-aa47-44cc-91dc-376dae51ef60","resolution":{"observed_at":"2026-08-06T00:09:46.242536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.229674Z","title":"Learning from multiple teacher networks,","venue":null,"work_id":"c7aac06f-759b-4283-8c0a-57f0e9bdee57","year":2017},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.473211Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:a2a73c173bc10978d08c7e9c93b5978223e3e67aaf695b5ebb3258feac90b87c","observation_id":"02a6d4e8-2988-4656-baec-a6ad88280aac","resolution":{"observed_at":"2026-08-06T00:09:46.233492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.220731Z","title":"Structured knowledge distillation for semantic segmentation,","venue":null,"work_id":"3414146d-10cf-49d3-a1b6-e509e06280a7","year":2019},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.476515Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:37be4859fd856174d8288e1b7408b1f0dd5f09aadd2171f24db50f293975461c","observation_id":"bf1a71c0-11a6-4f2d-b64e-aa61ebc4b7ad","resolution":{"observed_at":"2026-08-06T00:09:46.224347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.211524Z","title":"Double similarity distillation for semantic image segmentation,","venue":null,"work_id":"be7fdecd-9e6e-4224-b8dd-80d3c567099d","year":2021},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.480387Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:85d16eebd1438bb93cb89cff3445a4648b24a4d24d1dd0dbc3e28f94fb0bd0b0","observation_id":"ce128d14-3f1a-43e4-84e5-7ab19c03c3ba","resolution":{"observed_at":"2026-08-06T00:09:46.214993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.202974Z","title":"Prompting for multi-modal tracking,","venue":null,"work_id":"a58da4c5-a4e3-4027-900c-687e78957879","year":2022},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.483789Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:2db96eb50057dbdbc539bf8d568908c835d084041dc2526bfeea500009afde1f","observation_id":"1845dbce-2641-4d24-9c7f-5d78031a9d75","resolution":{"observed_at":"2026-08-06T00:09:46.205993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.193684Z","title":"Siam R-CNN: Visual tracking by re-detection,","venue":null,"work_id":"519ff56d-2b9a-4aba-8fff-730dd76983e4","year":2020},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.486742Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:79bf92ac81c85273eb394821fa6b5e48565bcde5614896e0f7352390a5ed0075","observation_id":"ae711d23-1b69-4479-8a40-efc840626b4c","resolution":{"observed_at":"2026-08-06T00:09:46.197170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.185370Z","title":"Transformer tracking,","venue":null,"work_id":"a1b0c973-ea29-4be6-a521-eb52764b8448","year":2021},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.490016Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:062f536eda37a1b4d34ae9d4f01dd6f6b0682b6156d25f063e58c364781ab0ee","observation_id":"5b9bcf1f-38db-401e-a9ca-93857f1ee7c0","resolution":{"observed_at":"2026-08-06T00:09:46.188404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.176516Z","title":"High-performance long-term tracking with meta-updater,","venue":null,"work_id":"697a44a3-0b87-401f-a389-947c77c66ff0","year":2020},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.492700Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:ea2c37de4ef8fcbbfa056d4ba2e17ac0ac76b588d81dfbefe3a816f9a875b88c","observation_id":"98426767-02ad-47f6-80f7-4b896535fe46","resolution":{"observed_at":"2026-08-06T00:09:46.179586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.167636Z","title":"Probabilistic regression for visual tracking,","venue":null,"work_id":"5a04b858-d2cb-4589-8452-1ce87595a189","year":2020},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.495709Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:a473539834c9fc3742b7d98256134b3c0ff1fb51b8ad6df4e07ff2336e9c2763","observation_id":"6d7af632-8ad0-4cff-b5a1-31259946f8b0","resolution":{"observed_at":"2026-08-06T00:09:46.170762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.157886Z","title":"Vital: Visual tracking via adversarial learning,","venue":null,"work_id":"ac58fdda-5326-4b17-9ad7-11daaa92aeff","year":2018},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.499114Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:304d94add560db1cc4b62605efdd1a92bee24c79bfd24e2713105f37f12c4dfc","observation_id":"beaea6ca-00ea-4e26-bbe2-e0f71133dcc7","resolution":{"observed_at":"2026-08-06T00:09:46.162240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.149095Z","title":"Learning multi-domain convolutional neural networks for visual tracking,","venue":null,"work_id":"7be3b463-a18f-4dcd-88c4-0253ccb8a58f","year":2016},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.501878Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:a95edcce8dbbf1a5cd6625427f79b930913fe7797c2f09beb11e79420a7162bf","observation_id":"77e3817e-642a-41c0-8ceb-9ee0075fca5b","resolution":{"observed_at":"2026-08-06T00:09:46.152519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.139918Z","title":"Atom: Accurate tracking by overlap maximization,","venue":null,"work_id":"84e2bdae-05d5-4847-86e0-f1d35d284e7c","year":2019},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.504621Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:ee6b1402651d8ba1b7637b63d9c2bfab2536069dbcab531b2d09063ed7c3dfd9","observation_id":"a71c6847-7eef-4bf6-afd6-1950873dc87e","resolution":{"observed_at":"2026-08-06T00:09:46.143466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.130073Z","title":"Learning spatio-temporal transformer for visual tracking,","venue":null,"work_id":"a1aa004a-1db9-41ad-be1e-b5b1f810b7a3","year":2021},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.508394Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:a93c3077fb67687e4e56167ee71f40177fe38f96cc0558c40d0533300443ea80","observation_id":"46d6e2b3-afce-4768-b022-b443641ef28f","resolution":{"observed_at":"2026-08-06T00:09:46.133776Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.120387Z","title":"Siamban: Target-aware tracking with siamese box adaptive network,","venue":null,"work_id":"042bbef2-722f-4a91-ac1f-43f160d8e61b","year":2022},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.511427Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:9a224e73fe8c68763521336b015d5084d3ab554e395c1fbbe53fa569821c6f1b","observation_id":"d3736a04-9935-4c84-aafc-549119065670","resolution":{"observed_at":"2026-08-06T00:09:46.123633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.110513Z","title":"Fast online object tracking and segmentation: A unifying approach,","venue":null,"work_id":"fa055539-9c65-4d0c-9995-fd976e4244ae","year":2019},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.514377Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:1af4c7c36b49b8cf17762f8b305833e65a9dac38e2db8a642164b6c97e46e34c","observation_id":"4413f9c7-2fd5-440b-bcf5-c8668d532482","resolution":{"observed_at":"2026-08-06T00:09:46.113825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.101465Z","title":"Attribute-based progressive fusion network for rgbt tracking,","venue":null,"work_id":"13c54ac8-f3fa-4d8f-bbbc-516bad0ba206","year":2022},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.517411Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:c73947276779041b537205bc1e8d20580c2b95558157beadad79ddeb4d1a6511","observation_id":"3de9d966-018c-47c4-ba50-a09836a49b3e","resolution":{"observed_at":"2026-08-06T00:09:46.104527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.092768Z","title":"Cross-modal pattern-propagation for rgb-t tracking,","venue":null,"work_id":"16f4e364-e7b0-4410-92aa-cf04da21871d","year":2020},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.520289Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:fac6dca6ebd71b79bc958213d17bf763121018d186fd18ec28ed64c20832ba4c","observation_id":"7810d613-66be-4904-8011-b5f7dd83115c","resolution":{"observed_at":"2026-08-06T00:09:46.095937Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.083127Z","title":"Jointly modeling motion and appearance cues for robust RGB-T tracking,","venue":null,"work_id":"13e566bf-eeae-43c3-aca2-d91cfe922b13","year":2021},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.523945Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:d0066fdc254989cbd14fb3f4f3a9505d02d351f8c8d7a967a444eb9758e0f502","observation_id":"effb7b87-3d4a-4f17-8c0d-3306b193efba","resolution":{"observed_at":"2026-08-06T00:09:46.087532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.073725Z","title":"Challenge-aware RGBT tracking,","venue":null,"work_id":"ab6b50d2-91a4-4a70-8117-0a4df044c88d","year":2020},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.526650Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:41ecb9336a202e0fe429273bdd439c37a3f5281edc5e431962a7729a01daf7a6","observation_id":"1c9c684d-efd0-4ee3-9a82-53e10f4f82f5","resolution":{"observed_at":"2026-08-06T00:09:46.077541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.064176Z","title":"Quality-aware feature aggregation network for robust RGBT tracking,","venue":null,"work_id":"d4326d9d-199e-415a-98ea-8c44bc75ac9c","year":2020},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.529642Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:5f0eb18e40e0329c9e266c1089994494aabcd724cfe22567d0d0bccbcc6b3de7","observation_id":"cdcc709a-05ed-44d4-87f2-65f8f76aca66","resolution":{"observed_at":"2026-08-06T00:09:46.068198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.055052Z","title":"Dense feature aggregation and pruning for RGBT tracking,","venue":null,"work_id":"6f191d06-8397-40bc-a0ad-6943f2876446","year":2019},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.532773Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:81e1b8d03816146a2bf5b032cd953527ae7ff59906855cd1823bf5aaccb73762","observation_id":"09459674-ab58-47ec-a941-97cf59504ec7","resolution":{"observed_at":"2026-08-06T00:09:46.058630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.045894Z","title":"Deep adaptive fusion network for high performance RGBT tracking,","venue":null,"work_id":"c143f6e5-56d9-4e6a-9857-b64d27bc79d5","year":2019},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.535769Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:92b7d51407afceb2ee37c79daede17c24f46bdacf4347482a765e6c416165526","observation_id":"9dd0b79d-0b98-4be5-92f1-58af100f4b20","resolution":{"observed_at":"2026-08-06T00:09:46.049287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.036920Z","title":"Object tracking in RGB-T videos using modal-aware attention network and competitive learning,","venue":null,"work_id":"c41d8153-9304-4764-8583-18cc3c301d7d","year":2020},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.539077Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:0424cfab9d4d1d9039e7c0815176d7686dbb41ab6c5b9683f9f0ecfa2f66d6b3","observation_id":"571e0ec0-4c8e-4f5a-a52c-2880d0ec09dc","resolution":{"observed_at":"2026-08-06T00:09:46.040053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.11023","last_updated":"2024-12-15T02:33:37Z","snapshot_observed_at":"2026-08-08T05:59:46.367303Z","submitted_at":"2024-12-15T02:33:37Z","title":"Exploring Enhanced Contextual Information for Video-Level Object Tracking","version":1},"cited_work":{"arxiv_id":"2412.11023","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.11023","snapshot_observed_at":"2026-08-06T00:09:45.800343Z","title":"Exploring Enhanced Contextual Information for Video-Level Object Tracking","venue":"cs.CV","work_id":"4dd0d550-b3b1-4a18-a5ed-5abf6afb1889","year":2024},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.541820Z"},"links":{"cited_paper":"/paper/2412.11023","citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:9063391c35ed9c687ceecfdae29cfbb3ee941fa73f09197e4d0182c5e3ec00f4","observation_id":"b3efdcb1-b796-499c-8c63-465fc0f908bf","resolution":{"observed_at":"2026-08-06T00:09:45.804317Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.027951Z","title":"Fast-itpn: Integrally pre-trained transformer pyramid network with token migration,","venue":null,"work_id":"50a26c7f-cc5f-4061-a3ee-e3f0ae6b1bd2","year":2024},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.545175Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:94b1a5a23910e4f36a2775d2d0eb1aad2354dd2fee5264e223fd041548604b12","observation_id":"254a2210-1dbb-44f1-b6e8-943f9fc73006","resolution":{"observed_at":"2026-08-06T00:09:46.031085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.018740Z","title":"Got-10k: A large high-diversity benchmark for generic object tracking in the wild,","venue":null,"work_id":"752dd4c5-0fa0-41a8-9070-9b510f087342","year":2019},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.548657Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:037e9485c7c59df25a583da6e21858ab042994743aa6d028e6c8f1e7f916b25e","observation_id":"a93cc7b4-9312-4abb-bae2-be1feafcf37c","resolution":{"observed_at":"2026-08-06T00:09:46.022165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:46.008780Z","title":"Trackingnet: A large-scale dataset and benchmark for object tracking in the wild,","venue":null,"work_id":"df3e70ac-1a9d-4178-980f-4cd2b3bf3815","year":2018},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.551551Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:cc2c45557da77f63c4af6b73bd7b8550d6a38f5c4d50a7c5adbffeb8ae7cd364","observation_id":"20dde008-6850-4ec5-8e0e-78b883a3ec5d","resolution":{"observed_at":"2026-08-06T00:09:46.012423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.999003Z","title":"Microsoft coco: Common objects in context,","venue":null,"work_id":"a233cf07-83c3-4349-ac42-5cd2ceccef21","year":2014},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.554534Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:04cba5bd6fdffb72dc0730f0f0e6f863bd37fa02d9f3e54d69c6a9abf23f95e9","observation_id":"fabd465d-7895-408a-a8f3-64c580da8891","resolution":{"observed_at":"2026-08-06T00:09:46.002594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.988617Z","title":"Lasot: A high-quality benchmark for large-scale single object tracking,","venue":null,"work_id":"f051161b-db9a-4e55-b8b1-8cb666f45062","year":2019},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.557637Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:ddd883310e3521f876b1765599569beb02366ca3856637091cb8bfbb7cf41447","observation_id":"9a8152e0-66cb-4870-9447-841c7846999a","resolution":{"observed_at":"2026-08-06T00:09:45.992729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.978811Z","title":"Lasher: A large-scale high-diversity benchmark for rgbt tracking,","venue":null,"work_id":"ecd0227f-db33-4783-a270-d557d6bb34ee","year":2021},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.560467Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:633ebe0ba7fa6ae81b875d2c6ea06638d7b583154aa78871b0648e27ab9c5c7c","observation_id":"0c998677-8d21-4256-8faf-2e10a0796100","resolution":{"observed_at":"2026-08-06T00:09:45.982352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.968842Z","title":"Visevent: Reliable object tracking via collaboration of frame and event flows,","venue":null,"work_id":"5b6edcce-4e30-4989-96f1-46209ca5865f","year":2023},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.563568Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:e959a2d1abc337c73c9cc8e3fbf01e2c776a5dc6e92e2353d27961ccacca21ec","observation_id":"4c9f9456-81ed-45d4-b9b0-cece5158950d","resolution":{"observed_at":"2026-08-06T00:09:45.972313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.959211Z","title":"Depthtrack: Unveiling the power of rgbd tracking,","venue":null,"work_id":"97fca642-290c-49f5-8b8e-ef41a0f43a54","year":2021},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.567327Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:82fda5c983b690230aab8898acbe5a22a10e7541b7521aa091ee6cd837c1a460","observation_id":"8d1bc65a-67f4-4dd3-912a-2c271df176e9","resolution":{"observed_at":"2026-08-06T00:09:45.963228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.949660Z","title":"Rgbd1k: A large-scale dataset and benchmark for rgb-d object tracking,","venue":null,"work_id":"c1e73062-1627-4062-a2df-744743c3fa2a","year":2023},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.570535Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:67f140ac456d73aa098db9d44db0b125060749bcf001b90a412800aee51ba021","observation_id":"d2180809-1177-4bad-910e-52387ef08fa4","resolution":{"observed_at":"2026-08-06T00:09:45.953438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.937632Z","title":"Correlation- aware deep tracking,","venue":null,"work_id":"a2474db5-87e2-439a-a672-8bddadec376f","year":2022},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.574169Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:7ce5e19087a59fde2fea71aaff82e4cc30358bbcd357d333fae74c668500636e","observation_id":"6bcbeb21-a9db-42c6-91a7-47b8a9534477","resolution":{"observed_at":"2026-08-06T00:09:45.942011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.926459Z","title":"The tenth visual object tracking vot2022 challenge results,","venue":null,"work_id":"9a920537-584a-4b46-a68d-c2c7654ca221","year":2023},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.577885Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:9c69e9e61111875a2d270c1c3974abec43b1e610dc3904052e5ba167ad4b9fa2","observation_id":"9004d9bc-2a79-447a-a5ce-f3a46dab6e61","resolution":{"observed_at":"2026-08-06T00:09:45.930704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.916376Z","title":"The eighth visual object tracking VOT2020 challenge results,","venue":null,"work_id":"0e537175-47ee-4798-888e-721a611df0e8","year":2020},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.580770Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:cf40ba7f36ab2663d19df61345d651d0cc49109a980fad0988b2e01509701be9","observation_id":"41b0b6c6-da42-4c8f-a945-913abe2204e2","resolution":{"observed_at":"2026-08-06T00:09:45.919732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.906621Z","title":"Dal: A deep depth-aware long-term tracker,","venue":null,"work_id":"a90cf7c8-9ccc-4d2a-9c96-a653ef212964","year":2021},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.583500Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:6e5b7cb9bd5e18d5e875f31be90081ebe12c22cdedf444667c770d2db7f60c11","observation_id":"30e40a7b-fdde-4c20-8f0d-7cf66a4d4e37","resolution":{"observed_at":"2026-08-06T00:09:45.909738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.897794Z","title":"The seventh visual object tracking VOT2019 challenge results,","venue":null,"work_id":"8fe4d25f-0dea-447e-bf66-0bc07f799301","year":2019},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.587062Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:5cc6e648a91e81af728da3a2b92c270d4500fbd33b5b190385a26388767766cf","observation_id":"f1998aeb-2155-4c80-8b22-dc647c00b748","resolution":{"observed_at":"2026-08-06T00:09:45.900796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.888829Z","title":"Context-aware three-dimensional mean-shift with occlusion handling for robust object tracking in RGB-D videos,","venue":null,"work_id":"94fd62e1-5f6c-4b3b-894e-18bcfb624dd3","year":2018},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.589843Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:07e104a87178bd3e1cedcd2a8d096745a8acaf3510f199d1a4b9315630b36eb2","observation_id":"59ac86b9-14a2-4235-aedd-db458844e1b2","resolution":{"observed_at":"2026-08-06T00:09:45.892407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.878678Z","title":"Learning discrimi- native model prediction for tracking,","venue":null,"work_id":"2c823686-aaf4-479b-99e3-90e9cbbf7afe","year":2019},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.592741Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:587eadd78b280e69b8fcb534d45acd63ac3dc052b92dfc0cae31b323eb69c8c6","observation_id":"294601b8-82a8-4689-bbba-a2b39591d0bb","resolution":{"observed_at":"2026-08-06T00:09:45.882609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.868890Z","title":"Channel-wise knowledge distillation for dense prediction,","venue":null,"work_id":"1c0968f4-2d60-4125-ad99-1dc2eac8ef1b","year":2021},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.595760Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:d7555e2632590c859b262aeed379805201c778f071cc30de81321008fe2429da","observation_id":"aefcdcd9-707b-41d0-aa87-10fbe1e6713d","resolution":{"observed_at":"2026-08-06T00:09:45.872267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6550","last_updated":"2015-03-27T11:52:28Z","snapshot_observed_at":"2026-07-06T04:04:16.777653Z","submitted_at":"2014-12-19T22:40:51Z","title":"FitNets: Hints for Thin Deep Nets","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6550","snapshot_observed_at":"2026-08-06T00:09:45.598653Z","title":"Fitnets: Hints for thin deep nets,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.598653Z"},"links":{"cited_paper":"/paper/1412.6550","citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:4e70b84b75d0b8aebabdb4579f363129d25f78768aa0ae5c90bed06155d0e017","observation_id":"a8a382e4-2bf8-4cbb-a52b-f67722b8e486","resolution":{"observed_at":"2026-08-06T00:09:45.598653Z","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":"2026-08-06T00:09:45.858701Z","title":"Similarity-preserving knowledge distillation,","venue":null,"work_id":"d271431b-42c2-4df6-acae-6866e26723eb","year":2019},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.602628Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:bbdf91115c0bb6898b8dcdb23e289cf085dd4dbdd2b0a3bb40b46c9fcd4216c8","observation_id":"4e5fea43-c5b8-4fee-a932-c17191b56a7f","resolution":{"observed_at":"2026-08-06T00:09:45.862165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.848588Z","title":"Customkd: Customizing large vision foundation for edge model improvement via knowledge distillation,","venue":null,"work_id":"564f9f04-f79c-43bd-9c32-d2c087bc6210","year":2025},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.605504Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:1492b8a7ad05a5601d65bedbe66c4bb7ade2c2cd454799b7c4385a7c2b9ad0a0","observation_id":"ae849a77-651f-4a6a-9a05-bdea66e2c9fc","resolution":{"observed_at":"2026-08-06T00:09:45.852508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05451","last_updated":"2025-03-24T06:34:34Z","snapshot_observed_at":"2026-07-06T17:41:42.995949Z","submitted_at":"2024-03-08T16:57:47Z","title":"Attention-guided Feature Distillation for Semantic Segmentation","version":3},"cited_work":{"arxiv_id":"2403.05451","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.05451","snapshot_observed_at":"2026-08-06T00:09:45.777773Z","title":"Attention-guided Feature Distillation for Semantic Segmentation","venue":"cs.CV","work_id":"7b879e62-f38c-4e5a-a48f-31783ffe8fe4","year":2024},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.608192Z"},"links":{"cited_paper":"/paper/2403.05451","citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:772215526f5401b0e59d704072ea68c9bc69a7ca16df9e56e1904b9e62ea120a","observation_id":"016d73eb-3986-4d8f-b800-2f0ec16e6331","resolution":{"observed_at":"2026-08-06T00:09:45.783428Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.838972Z","title":"Rethinking knowledge distillation with raw features for semantic segmentation,","venue":null,"work_id":"0ae173e7-9105-4e8f-a61b-ecc07d4522a4","year":2024},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.612443Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:56062db07340a64629a2f681358a5fc71ce91206699e20bf064526247aa54df9","observation_id":"dc3f0682-4722-48bd-86ad-cdb508d155d0","resolution":{"observed_at":"2026-08-06T00:09:45.842222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T00:09:45.616325Z","title":"Simple semi-supervised knowledge distillation from vision-language models via dual-head optimization,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.616325Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:1389ba7c36be0535c725b1db839f59892a1306b5e88f4f8a586f7b87e6e73485","observation_id":"ea853971-8cb0-4f46-9d26-0a143d057e24","resolution":{"observed_at":"2026-08-06T00:09:45.616325Z","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":"2026-08-06T00:09:45.619182Z","title":"Generative adversarial networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:45.619182Z"},"links":{"citing_paper":"/paper/2608.01488"},"observation_digest":"sha256:1023a8a1f0323d814b62aa28a11769ca0b51ce8f4d6a7edf4d65d6c52b879e34","observation_id":"43f71132-027a-4c9a-8d3a-73bae4af8369","resolution":{"observed_at":"2026-08-06T00:09:45.619182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.01488","last_updated":"2026-08-02T20:41:33Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T11:10:11.754401Z","submitted_at":"2026-08-02T20:41:33Z","title":"Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning"},"reference_resolution":{"displayed":74,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":2,"verified_fuzzy":66},"total_outbound_references":74},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 0 inbound Pith citation observations for arXiv:2608.01488."}