{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2WKMN55E65MHLCN4G2WO4IQZER","short_pith_number":"pith:2WKMN55E","schema_version":"1.0","canonical_sha256":"d594c6f7a4f7587589bc36acee221924447da143de9d32dbb77b4899410ca6a5","source":{"kind":"arxiv","id":"2505.02549","version":2},"attestation_state":"computed","paper":{"title":"Robust Duality Learning for Unsupervised Visible-Infrared Person Re-Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Dezhong Peng, Peng Hu, Xi Peng, Yang Qin, Yongxiang Li, Yuan Sun","submitted_at":"2025-05-05T10:36:52Z","abstract_excerpt":"Unsupervised visible-infrared person re-identification (UVI-ReID) aims to retrieve pedestrian images across different modalities without costly annotations, but faces challenges due to the modality gap and lack of supervision. Existing methods often adopt self-training with clustering-generated pseudo-labels but implicitly assume these labels are always correct. In practice, however, this assumption fails due to inevitable pseudo-label noise, which hinders model learning. To address this, we introduce a new learning paradigm that explicitly considers Pseudo-Label Noise (PLN), characterized by "},"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":"2505.02549","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-05T10:36:52Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"de3170839940b0b79f03f8b256690995d5925d7ae109444386b1671e5b639265","abstract_canon_sha256":"fc30660ac46445d1d01c892743f3fb4b6cc0f5fb24ec792a455c605c2cfd7c29"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:59:10.394801Z","signature_b64":"ifV7z+blsPdnOMDlg0TnOX8GNj5mgi05nVepby54EgUHhPMP1CTp4TcYRBnn/JwxM/P5ug9BmgMZEfVe31TZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d594c6f7a4f7587589bc36acee221924447da143de9d32dbb77b4899410ca6a5","last_reissued_at":"2026-07-05T10:59:10.394299Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:59:10.394299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Duality Learning for Unsupervised Visible-Infrared Person Re-Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Dezhong Peng, Peng Hu, Xi Peng, Yang Qin, Yongxiang Li, Yuan Sun","submitted_at":"2025-05-05T10:36:52Z","abstract_excerpt":"Unsupervised visible-infrared person re-identification (UVI-ReID) aims to retrieve pedestrian images across different modalities without costly annotations, but faces challenges due to the modality gap and lack of supervision. Existing methods often adopt self-training with clustering-generated pseudo-labels but implicitly assume these labels are always correct. In practice, however, this assumption fails due to inevitable pseudo-label noise, which hinders model learning. To address this, we introduce a new learning paradigm that explicitly considers Pseudo-Label Noise (PLN), characterized by "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.02549","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/2505.02549/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":"2505.02549","created_at":"2026-07-05T10:59:10.394371+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.02549v2","created_at":"2026-07-05T10:59:10.394371+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.02549","created_at":"2026-07-05T10:59:10.394371+00:00"},{"alias_kind":"pith_short_12","alias_value":"2WKMN55E65MH","created_at":"2026-07-05T10:59:10.394371+00:00"},{"alias_kind":"pith_short_16","alias_value":"2WKMN55E65MHLCN4","created_at":"2026-07-05T10:59:10.394371+00:00"},{"alias_kind":"pith_short_8","alias_value":"2WKMN55E","created_at":"2026-07-05T10:59:10.394371+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2WKMN55E65MHLCN4G2WO4IQZER","json":"https://pith.science/pith/2WKMN55E65MHLCN4G2WO4IQZER.json","graph_json":"https://pith.science/api/pith-number/2WKMN55E65MHLCN4G2WO4IQZER/graph.json","events_json":"https://pith.science/api/pith-number/2WKMN55E65MHLCN4G2WO4IQZER/events.json","paper":"https://pith.science/paper/2WKMN55E"},"agent_actions":{"view_html":"https://pith.science/pith/2WKMN55E65MHLCN4G2WO4IQZER","download_json":"https://pith.science/pith/2WKMN55E65MHLCN4G2WO4IQZER.json","view_paper":"https://pith.science/paper/2WKMN55E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.02549&json=true","fetch_graph":"https://pith.science/api/pith-number/2WKMN55E65MHLCN4G2WO4IQZER/graph.json","fetch_events":"https://pith.science/api/pith-number/2WKMN55E65MHLCN4G2WO4IQZER/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2WKMN55E65MHLCN4G2WO4IQZER/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2WKMN55E65MHLCN4G2WO4IQZER/action/storage_attestation","attest_author":"https://pith.science/pith/2WKMN55E65MHLCN4G2WO4IQZER/action/author_attestation","sign_citation":"https://pith.science/pith/2WKMN55E65MHLCN4G2WO4IQZER/action/citation_signature","submit_replication":"https://pith.science/pith/2WKMN55E65MHLCN4G2WO4IQZER/action/replication_record"}},"created_at":"2026-07-05T10:59:10.394371+00:00","updated_at":"2026-07-05T10:59:10.394371+00:00"}