{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:K5YLXXSOZDTKF7IZ72RYVWTWHE","short_pith_number":"pith:K5YLXXSO","schema_version":"1.0","canonical_sha256":"5770bbde4ec8e6a2fd19fea38ada76392d0ff961bcef9b32402b002660a9378a","source":{"kind":"arxiv","id":"2302.13840","version":1},"attestation_state":"computed","paper":{"title":"Target-Aware Tracking with Long-term Context Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Canlong Zhang, Kaijie He, Sheng Xie, Zhiwen Wang, Zhixin Li","submitted_at":"2023-02-27T14:40:58Z","abstract_excerpt":"Most deep trackers still follow the guidance of the siamese paradigms and use a template that contains only the target without any contextual information, which makes it difficult for the tracker to cope with large appearance changes, rapid target movement, and attraction from similar objects. To alleviate the above problem, we propose a long-term context attention (LCA) module that can perform extensive information fusion on the target and its context from long-term frames, and calculate the target correlation while enhancing target features. The complete contextual information contains the l"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2302.13840","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-27T14:40:58Z","cross_cats_sorted":[],"title_canon_sha256":"f4434cfa301892a4a0d3b2cf548596c466a4cc00856e3625357b51bfc18cf8ec","abstract_canon_sha256":"27d33da741f46a58c9f38ea5096ed6737954a0f42e20c915f2b80de99e277730"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:45:51.495602Z","signature_b64":"2TQ7/014QBfiMDDPL9WvrtdF18aylJ/awRHjy5HH2L0rB/sDh8nmK2zJEZUOnsLktDYtg3qz89GZM9kQadekAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5770bbde4ec8e6a2fd19fea38ada76392d0ff961bcef9b32402b002660a9378a","last_reissued_at":"2026-07-05T05:45:51.495251Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:45:51.495251Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Target-Aware Tracking with Long-term Context Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Canlong Zhang, Kaijie He, Sheng Xie, Zhiwen Wang, Zhixin Li","submitted_at":"2023-02-27T14:40:58Z","abstract_excerpt":"Most deep trackers still follow the guidance of the siamese paradigms and use a template that contains only the target without any contextual information, which makes it difficult for the tracker to cope with large appearance changes, rapid target movement, and attraction from similar objects. To alleviate the above problem, we propose a long-term context attention (LCA) module that can perform extensive information fusion on the target and its context from long-term frames, and calculate the target correlation while enhancing target features. The complete contextual information contains the l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.13840","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2302.13840/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2302.13840","created_at":"2026-07-05T05:45:51.495306+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.13840v1","created_at":"2026-07-05T05:45:51.495306+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.13840","created_at":"2026-07-05T05:45:51.495306+00:00"},{"alias_kind":"pith_short_12","alias_value":"K5YLXXSOZDTK","created_at":"2026-07-05T05:45:51.495306+00:00"},{"alias_kind":"pith_short_16","alias_value":"K5YLXXSOZDTKF7IZ","created_at":"2026-07-05T05:45:51.495306+00:00"},{"alias_kind":"pith_short_8","alias_value":"K5YLXXSO","created_at":"2026-07-05T05:45:51.495306+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.18855","citing_title":"Improving Accuracy and Generalization for Efficient Visual Tracking","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K5YLXXSOZDTKF7IZ72RYVWTWHE","json":"https://pith.science/pith/K5YLXXSOZDTKF7IZ72RYVWTWHE.json","graph_json":"https://pith.science/api/pith-number/K5YLXXSOZDTKF7IZ72RYVWTWHE/graph.json","events_json":"https://pith.science/api/pith-number/K5YLXXSOZDTKF7IZ72RYVWTWHE/events.json","paper":"https://pith.science/paper/K5YLXXSO"},"agent_actions":{"view_html":"https://pith.science/pith/K5YLXXSOZDTKF7IZ72RYVWTWHE","download_json":"https://pith.science/pith/K5YLXXSOZDTKF7IZ72RYVWTWHE.json","view_paper":"https://pith.science/paper/K5YLXXSO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.13840&json=true","fetch_graph":"https://pith.science/api/pith-number/K5YLXXSOZDTKF7IZ72RYVWTWHE/graph.json","fetch_events":"https://pith.science/api/pith-number/K5YLXXSOZDTKF7IZ72RYVWTWHE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K5YLXXSOZDTKF7IZ72RYVWTWHE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K5YLXXSOZDTKF7IZ72RYVWTWHE/action/storage_attestation","attest_author":"https://pith.science/pith/K5YLXXSOZDTKF7IZ72RYVWTWHE/action/author_attestation","sign_citation":"https://pith.science/pith/K5YLXXSOZDTKF7IZ72RYVWTWHE/action/citation_signature","submit_replication":"https://pith.science/pith/K5YLXXSOZDTKF7IZ72RYVWTWHE/action/replication_record"}},"created_at":"2026-07-05T05:45:51.495306+00:00","updated_at":"2026-07-05T05:45:51.495306+00:00"}