{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:YLORBQYZI65P2LU3VBUZATJYFC","short_pith_number":"pith:YLORBQYZ","schema_version":"1.0","canonical_sha256":"c2dd10c31947bafd2e9ba869904d3828a847420009083fdb3a4a49738517b060","source":{"kind":"arxiv","id":"1809.04320","version":2},"attestation_state":"computed","paper":{"title":"Learning regression and verification networks for long-term visual tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dong Wang, Huchuan Lu, Jinqing Qi, Lijun Wang, Yunhua Zhang","submitted_at":"2018-09-12T09:13:48Z","abstract_excerpt":"Compared with short-term tracking, the long-term tracking task requires determining the tracked object is present or absent, and then estimating the accurate bounding box if present or conducting image-wide re-detection if absent. Until now, few attempts have been done although this task is much closer to designing practical tracking systems. In this work, we propose a novel long-term tracking framework based on deep regression and verification networks. The offline-trained regression model is designed using the object-aware feature fusion and region proposal networks to generate a series of c"},"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":"1809.04320","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-12T09:13:48Z","cross_cats_sorted":[],"title_canon_sha256":"406785ff970bed771459943cd654380f803c193f51a6b4060cfd5895fcd5eb17","abstract_canon_sha256":"096c83f52434eac214f718152a0901c248342dbb90f2754f99a1269db5238bca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:29.679137Z","signature_b64":"hXEQIDLWBTV+7AZ0N+5nSfI+M0JtB5zhq1p8pb0vcs74BOGX0jhtGVT14zK+mU3pVUzFDHHoCb+RIdfGosapDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c2dd10c31947bafd2e9ba869904d3828a847420009083fdb3a4a49738517b060","last_reissued_at":"2026-05-18T00:00:29.678687Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:29.678687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning regression and verification networks for long-term visual tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dong Wang, Huchuan Lu, Jinqing Qi, Lijun Wang, Yunhua Zhang","submitted_at":"2018-09-12T09:13:48Z","abstract_excerpt":"Compared with short-term tracking, the long-term tracking task requires determining the tracked object is present or absent, and then estimating the accurate bounding box if present or conducting image-wide re-detection if absent. Until now, few attempts have been done although this task is much closer to designing practical tracking systems. In this work, we propose a novel long-term tracking framework based on deep regression and verification networks. The offline-trained regression model is designed using the object-aware feature fusion and region proposal networks to generate a series of c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04320","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1809.04320","created_at":"2026-05-18T00:00:29.678759+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.04320v2","created_at":"2026-05-18T00:00:29.678759+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04320","created_at":"2026-05-18T00:00:29.678759+00:00"},{"alias_kind":"pith_short_12","alias_value":"YLORBQYZI65P","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"YLORBQYZI65P2LU3","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"YLORBQYZ","created_at":"2026-05-18T12:33:04.347982+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.00618","citing_title":"CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark","ref_index":46,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YLORBQYZI65P2LU3VBUZATJYFC","json":"https://pith.science/pith/YLORBQYZI65P2LU3VBUZATJYFC.json","graph_json":"https://pith.science/api/pith-number/YLORBQYZI65P2LU3VBUZATJYFC/graph.json","events_json":"https://pith.science/api/pith-number/YLORBQYZI65P2LU3VBUZATJYFC/events.json","paper":"https://pith.science/paper/YLORBQYZ"},"agent_actions":{"view_html":"https://pith.science/pith/YLORBQYZI65P2LU3VBUZATJYFC","download_json":"https://pith.science/pith/YLORBQYZI65P2LU3VBUZATJYFC.json","view_paper":"https://pith.science/paper/YLORBQYZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.04320&json=true","fetch_graph":"https://pith.science/api/pith-number/YLORBQYZI65P2LU3VBUZATJYFC/graph.json","fetch_events":"https://pith.science/api/pith-number/YLORBQYZI65P2LU3VBUZATJYFC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YLORBQYZI65P2LU3VBUZATJYFC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YLORBQYZI65P2LU3VBUZATJYFC/action/storage_attestation","attest_author":"https://pith.science/pith/YLORBQYZI65P2LU3VBUZATJYFC/action/author_attestation","sign_citation":"https://pith.science/pith/YLORBQYZI65P2LU3VBUZATJYFC/action/citation_signature","submit_replication":"https://pith.science/pith/YLORBQYZI65P2LU3VBUZATJYFC/action/replication_record"}},"created_at":"2026-05-18T00:00:29.678759+00:00","updated_at":"2026-05-18T00:00:29.678759+00:00"}