{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:JTYOEVW4LNHRDRLNJ53TLSEOY2","short_pith_number":"pith:JTYOEVW4","schema_version":"1.0","canonical_sha256":"4cf0e256dc5b4f11c56d4f7735c88ec6a4f125b609986dea6285027e0f3f084b","source":{"kind":"arxiv","id":"1808.07301","version":1},"attestation_state":"computed","paper":{"title":"Deep Association Learning for Unsupervised Video Person Re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Shaogang Gong, Xiatian Zhu, Yanbei Chen","submitted_at":"2018-08-22T10:16:43Z","abstract_excerpt":"Deep learning methods have started to dominate the research progress of video-based person re-identification (re-id). However, existing methods mostly consider supervised learning, which requires exhaustive manual efforts for labelling cross-view pairwise data. Therefore, they severely lack scalability and practicality in real-world video surveillance applications. In this work, to address the video person re-id task, we formulate a novel Deep Association Learning (DAL) scheme, the first end-to-end deep learning method using none of the identity labels in model initialisation and training. DAL"},"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":"1808.07301","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-22T10:16:43Z","cross_cats_sorted":[],"title_canon_sha256":"ad968ea140b3fac44adb578f0a99578af04a7c411151b70add17d496c036dfa8","abstract_canon_sha256":"e2de5712bde988c1ff96d84f3a87405bac8890daeb99e23dc2029e65f9e50db5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:29.258773Z","signature_b64":"mFpvJz9vzyuYsZW2kinilOmnw//U96J8q3VswF12RGCrW80s9Ue3lloVaHYC0FdWj2tzKDVCJoIiZlQjasRUDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4cf0e256dc5b4f11c56d4f7735c88ec6a4f125b609986dea6285027e0f3f084b","last_reissued_at":"2026-05-18T00:07:29.258059Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:29.258059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Association Learning for Unsupervised Video Person Re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Shaogang Gong, Xiatian Zhu, Yanbei Chen","submitted_at":"2018-08-22T10:16:43Z","abstract_excerpt":"Deep learning methods have started to dominate the research progress of video-based person re-identification (re-id). However, existing methods mostly consider supervised learning, which requires exhaustive manual efforts for labelling cross-view pairwise data. Therefore, they severely lack scalability and practicality in real-world video surveillance applications. In this work, to address the video person re-id task, we formulate a novel Deep Association Learning (DAL) scheme, the first end-to-end deep learning method using none of the identity labels in model initialisation and training. DAL"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.07301","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":""},"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":"1808.07301","created_at":"2026-05-18T00:07:29.258161+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.07301v1","created_at":"2026-05-18T00:07:29.258161+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.07301","created_at":"2026-05-18T00:07:29.258161+00:00"},{"alias_kind":"pith_short_12","alias_value":"JTYOEVW4LNHR","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"JTYOEVW4LNHRDRLN","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"JTYOEVW4","created_at":"2026-05-18T12:32:31.084164+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2508.17431","citing_title":"FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2508.17431","citing_title":"FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JTYOEVW4LNHRDRLNJ53TLSEOY2","json":"https://pith.science/pith/JTYOEVW4LNHRDRLNJ53TLSEOY2.json","graph_json":"https://pith.science/api/pith-number/JTYOEVW4LNHRDRLNJ53TLSEOY2/graph.json","events_json":"https://pith.science/api/pith-number/JTYOEVW4LNHRDRLNJ53TLSEOY2/events.json","paper":"https://pith.science/paper/JTYOEVW4"},"agent_actions":{"view_html":"https://pith.science/pith/JTYOEVW4LNHRDRLNJ53TLSEOY2","download_json":"https://pith.science/pith/JTYOEVW4LNHRDRLNJ53TLSEOY2.json","view_paper":"https://pith.science/paper/JTYOEVW4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.07301&json=true","fetch_graph":"https://pith.science/api/pith-number/JTYOEVW4LNHRDRLNJ53TLSEOY2/graph.json","fetch_events":"https://pith.science/api/pith-number/JTYOEVW4LNHRDRLNJ53TLSEOY2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JTYOEVW4LNHRDRLNJ53TLSEOY2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JTYOEVW4LNHRDRLNJ53TLSEOY2/action/storage_attestation","attest_author":"https://pith.science/pith/JTYOEVW4LNHRDRLNJ53TLSEOY2/action/author_attestation","sign_citation":"https://pith.science/pith/JTYOEVW4LNHRDRLNJ53TLSEOY2/action/citation_signature","submit_replication":"https://pith.science/pith/JTYOEVW4LNHRDRLNJ53TLSEOY2/action/replication_record"}},"created_at":"2026-05-18T00:07:29.258161+00:00","updated_at":"2026-05-18T00:07:29.258161+00:00"}