{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CFRLF6H6SHO3CNWSIDLSCRAD26","short_pith_number":"pith:CFRLF6H6","schema_version":"1.0","canonical_sha256":"1162b2f8fe91ddb136d240d7214403d791d65394f14fa7ed11c266152897e804","source":{"kind":"arxiv","id":"2103.15890","version":4},"attestation_state":"computed","paper":{"title":"Learning Domain Invariant Representations for Generalizable Person Re-Identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Da Li, Liang Wang, Tieniu Tan, Yi-Fan Zhang, Zhang Zhang, Zhen Jia","submitted_at":"2021-03-29T18:59:48Z","abstract_excerpt":"Generalizable person Re-Identification (ReID) has attracted growing attention in recent computer vision community. In this work, we construct a structural causal model among identity labels, identity-specific factors (clothes/shoes color etc), and domain-specific factors (background, viewpoints etc). According to the causal analysis, we propose a novel Domain Invariant Representation Learning for generalizable person Re-Identification (DIR-ReID) framework. Specifically, we first propose to disentangle the identity-specific and domain-specific feature spaces, based on which we propose an effect"},"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":"2103.15890","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-29T18:59:48Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c31577eeb8c83c7194fc3f5203780273b5df15fcee40e28e8a06d393921c56f3","abstract_canon_sha256":"fc160a196743a2a2b3140a79a0863ad29d0bc508e8f562b6d47f5d625c23bf00"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:26:01.278885Z","signature_b64":"xoDtNeCwzr6ji7VaHe9OIHr6hcVI98Fyb2jCW/xtlAgy4dAuDELG8zMRuwLcepZeeCZNeP+9OlMCZGdfzCBACg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1162b2f8fe91ddb136d240d7214403d791d65394f14fa7ed11c266152897e804","last_reissued_at":"2026-07-05T05:26:01.278451Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:26:01.278451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Domain Invariant Representations for Generalizable Person Re-Identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Da Li, Liang Wang, Tieniu Tan, Yi-Fan Zhang, Zhang Zhang, Zhen Jia","submitted_at":"2021-03-29T18:59:48Z","abstract_excerpt":"Generalizable person Re-Identification (ReID) has attracted growing attention in recent computer vision community. In this work, we construct a structural causal model among identity labels, identity-specific factors (clothes/shoes color etc), and domain-specific factors (background, viewpoints etc). According to the causal analysis, we propose a novel Domain Invariant Representation Learning for generalizable person Re-Identification (DIR-ReID) framework. Specifically, we first propose to disentangle the identity-specific and domain-specific feature spaces, based on which we propose an effect"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.15890","kind":"arxiv","version":4},"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/2103.15890/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":"2103.15890","created_at":"2026-07-05T05:26:01.278513+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.15890v4","created_at":"2026-07-05T05:26:01.278513+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.15890","created_at":"2026-07-05T05:26:01.278513+00:00"},{"alias_kind":"pith_short_12","alias_value":"CFRLF6H6SHO3","created_at":"2026-07-05T05:26:01.278513+00:00"},{"alias_kind":"pith_short_16","alias_value":"CFRLF6H6SHO3CNWS","created_at":"2026-07-05T05:26:01.278513+00:00"},{"alias_kind":"pith_short_8","alias_value":"CFRLF6H6","created_at":"2026-07-05T05:26:01.278513+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.20540","citing_title":"Causality and \"In-the-Wild\" Video-Based Person Re-ID: A Survey","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CFRLF6H6SHO3CNWSIDLSCRAD26","json":"https://pith.science/pith/CFRLF6H6SHO3CNWSIDLSCRAD26.json","graph_json":"https://pith.science/api/pith-number/CFRLF6H6SHO3CNWSIDLSCRAD26/graph.json","events_json":"https://pith.science/api/pith-number/CFRLF6H6SHO3CNWSIDLSCRAD26/events.json","paper":"https://pith.science/paper/CFRLF6H6"},"agent_actions":{"view_html":"https://pith.science/pith/CFRLF6H6SHO3CNWSIDLSCRAD26","download_json":"https://pith.science/pith/CFRLF6H6SHO3CNWSIDLSCRAD26.json","view_paper":"https://pith.science/paper/CFRLF6H6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.15890&json=true","fetch_graph":"https://pith.science/api/pith-number/CFRLF6H6SHO3CNWSIDLSCRAD26/graph.json","fetch_events":"https://pith.science/api/pith-number/CFRLF6H6SHO3CNWSIDLSCRAD26/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CFRLF6H6SHO3CNWSIDLSCRAD26/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CFRLF6H6SHO3CNWSIDLSCRAD26/action/storage_attestation","attest_author":"https://pith.science/pith/CFRLF6H6SHO3CNWSIDLSCRAD26/action/author_attestation","sign_citation":"https://pith.science/pith/CFRLF6H6SHO3CNWSIDLSCRAD26/action/citation_signature","submit_replication":"https://pith.science/pith/CFRLF6H6SHO3CNWSIDLSCRAD26/action/replication_record"}},"created_at":"2026-07-05T05:26:01.278513+00:00","updated_at":"2026-07-05T05:26:01.278513+00:00"}