{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:42Q6GPIUYIEBMDCNR3GZQJTMAF","short_pith_number":"pith:42Q6GPIU","schema_version":"1.0","canonical_sha256":"e6a1e33d14c208160c4d8ecd98266c014e026d9d12dc32027e525bb396063f2e","source":{"kind":"arxiv","id":"2509.16635","version":2},"attestation_state":"computed","paper":{"title":"Towards Anytime Retrieval: A Benchmark for Anytime Person Re-Identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin Liu, Jiaze Li, Mang Ye, Nenghai Yu, Qi Chu, Qinhong Yang, Tao Gong, Xulin Li, Yan Lu","submitted_at":"2025-09-20T11:20:22Z","abstract_excerpt":"In real applications, person re-identification (ReID) is expected to retrieve the target person at any time, including both daytime and nighttime, ranging from short-term to long-term. However, existing ReID tasks and datasets can not meet this requirement, as they are constrained by available time and only provide training and evaluation for specific scenarios. Therefore, we investigate a new task called Anytime Person Re-identification (AT-ReID), which aims to achieve effective retrieval in multiple scenarios based on variations in time. To address the AT-ReID problem, we collect the first 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":"2509.16635","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-20T11:20:22Z","cross_cats_sorted":[],"title_canon_sha256":"763922571c1ed4a03eb2dbdec8671e9a75ca879d93124a717291d831a109b05b","abstract_canon_sha256":"99d3ddd252cc3f664959955bf9746471c7982ee720fcb6a669598400cf2bf021"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T01:03:33.684290Z","signature_b64":"V7JJDpGQG+lb86zK+IZoRRKe4KdDWmXRF06aAz2eqbGSjbeL2fRqJW4W32FzTTpBd5k2Jy6xXBobjNkcsc7zAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e6a1e33d14c208160c4d8ecd98266c014e026d9d12dc32027e525bb396063f2e","last_reissued_at":"2026-06-02T01:03:33.683649Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T01:03:33.683649Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Anytime Retrieval: A Benchmark for Anytime Person Re-Identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin Liu, Jiaze Li, Mang Ye, Nenghai Yu, Qi Chu, Qinhong Yang, Tao Gong, Xulin Li, Yan Lu","submitted_at":"2025-09-20T11:20:22Z","abstract_excerpt":"In real applications, person re-identification (ReID) is expected to retrieve the target person at any time, including both daytime and nighttime, ranging from short-term to long-term. However, existing ReID tasks and datasets can not meet this requirement, as they are constrained by available time and only provide training and evaluation for specific scenarios. Therefore, we investigate a new task called Anytime Person Re-identification (AT-ReID), which aims to achieve effective retrieval in multiple scenarios based on variations in time. To address the AT-ReID problem, we collect the first l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.16635","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.16635/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":"2509.16635","created_at":"2026-06-02T01:03:33.683737+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.16635v2","created_at":"2026-06-02T01:03:33.683737+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.16635","created_at":"2026-06-02T01:03:33.683737+00:00"},{"alias_kind":"pith_short_12","alias_value":"42Q6GPIUYIEB","created_at":"2026-06-02T01:03:33.683737+00:00"},{"alias_kind":"pith_short_16","alias_value":"42Q6GPIUYIEBMDCN","created_at":"2026-06-02T01:03:33.683737+00:00"},{"alias_kind":"pith_short_8","alias_value":"42Q6GPIU","created_at":"2026-06-02T01:03:33.683737+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2604.15090","citing_title":"Beyond Visual Cues: Semantic-Driven Token Filtering and Expert Routing for Anytime Person ReID","ref_index":1,"is_internal_anchor":true},{"citing_arxiv_id":"2604.15979","citing_title":"MMGait: Towards Multi-Modal Gait Recognition","ref_index":47,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/42Q6GPIUYIEBMDCNR3GZQJTMAF","json":"https://pith.science/pith/42Q6GPIUYIEBMDCNR3GZQJTMAF.json","graph_json":"https://pith.science/api/pith-number/42Q6GPIUYIEBMDCNR3GZQJTMAF/graph.json","events_json":"https://pith.science/api/pith-number/42Q6GPIUYIEBMDCNR3GZQJTMAF/events.json","paper":"https://pith.science/paper/42Q6GPIU"},"agent_actions":{"view_html":"https://pith.science/pith/42Q6GPIUYIEBMDCNR3GZQJTMAF","download_json":"https://pith.science/pith/42Q6GPIUYIEBMDCNR3GZQJTMAF.json","view_paper":"https://pith.science/paper/42Q6GPIU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.16635&json=true","fetch_graph":"https://pith.science/api/pith-number/42Q6GPIUYIEBMDCNR3GZQJTMAF/graph.json","fetch_events":"https://pith.science/api/pith-number/42Q6GPIUYIEBMDCNR3GZQJTMAF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/42Q6GPIUYIEBMDCNR3GZQJTMAF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/42Q6GPIUYIEBMDCNR3GZQJTMAF/action/storage_attestation","attest_author":"https://pith.science/pith/42Q6GPIUYIEBMDCNR3GZQJTMAF/action/author_attestation","sign_citation":"https://pith.science/pith/42Q6GPIUYIEBMDCNR3GZQJTMAF/action/citation_signature","submit_replication":"https://pith.science/pith/42Q6GPIUYIEBMDCNR3GZQJTMAF/action/replication_record"}},"created_at":"2026-06-02T01:03:33.683737+00:00","updated_at":"2026-06-02T01:03:33.683737+00:00"}