{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:ATL65RN6RVHQ763FAYVMWEBI33","short_pith_number":"pith:ATL65RN6","schema_version":"1.0","canonical_sha256":"04d7eec5be8d4f0ffb65062acb1028def9f8821fe819b9ae3a97088051b1f60b","source":{"kind":"arxiv","id":"1603.02139","version":1},"attestation_state":"computed","paper":{"title":"Learning a Discriminative Null Space for Person Re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Li Zhang, Shaogang Gong, Tao Xiang","submitted_at":"2016-03-07T16:26:07Z","abstract_excerpt":"Most existing person re-identification (re-id) methods focus on learning the optimal distance metrics across camera views. Typically a person's appearance is represented using features of thousands of dimensions, whilst only hundreds of training samples are available due to the difficulties in collecting matched training images. With the number of training samples much smaller than the feature dimension, the existing methods thus face the classic small sample size (SSS) problem and have to resort to dimensionality reduction techniques and/or matrix regularisation, which lead to loss of discrim"},"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":"1603.02139","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-07T16:26:07Z","cross_cats_sorted":[],"title_canon_sha256":"641897378b3cba29859237193be77c348ec50b6be366115fc9bf9e9f5ae00427","abstract_canon_sha256":"11f4318391ea659f997bdaa01e6e8e3c46a9238b60c3ecef8082cddb3b822279"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:19:31.554480Z","signature_b64":"EasPK8R1ncLsFqwmOGfkfJW3jZ5mOsTRdT03IdwADFYKQf6cwydz1Q4X6YNX1U3Q7+8E4vEy5NQqHgw7x1uWCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04d7eec5be8d4f0ffb65062acb1028def9f8821fe819b9ae3a97088051b1f60b","last_reissued_at":"2026-05-18T01:19:31.553861Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:19:31.553861Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning a Discriminative Null Space for Person Re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Li Zhang, Shaogang Gong, Tao Xiang","submitted_at":"2016-03-07T16:26:07Z","abstract_excerpt":"Most existing person re-identification (re-id) methods focus on learning the optimal distance metrics across camera views. Typically a person's appearance is represented using features of thousands of dimensions, whilst only hundreds of training samples are available due to the difficulties in collecting matched training images. With the number of training samples much smaller than the feature dimension, the existing methods thus face the classic small sample size (SSS) problem and have to resort to dimensionality reduction techniques and/or matrix regularisation, which lead to loss of discrim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.02139","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":"1603.02139","created_at":"2026-05-18T01:19:31.553948+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.02139v1","created_at":"2026-05-18T01:19:31.553948+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.02139","created_at":"2026-05-18T01:19:31.553948+00:00"},{"alias_kind":"pith_short_12","alias_value":"ATL65RN6RVHQ","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_16","alias_value":"ATL65RN6RVHQ763F","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_8","alias_value":"ATL65RN6","created_at":"2026-05-18T12:30:07.202191+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/ATL65RN6RVHQ763FAYVMWEBI33","json":"https://pith.science/pith/ATL65RN6RVHQ763FAYVMWEBI33.json","graph_json":"https://pith.science/api/pith-number/ATL65RN6RVHQ763FAYVMWEBI33/graph.json","events_json":"https://pith.science/api/pith-number/ATL65RN6RVHQ763FAYVMWEBI33/events.json","paper":"https://pith.science/paper/ATL65RN6"},"agent_actions":{"view_html":"https://pith.science/pith/ATL65RN6RVHQ763FAYVMWEBI33","download_json":"https://pith.science/pith/ATL65RN6RVHQ763FAYVMWEBI33.json","view_paper":"https://pith.science/paper/ATL65RN6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.02139&json=true","fetch_graph":"https://pith.science/api/pith-number/ATL65RN6RVHQ763FAYVMWEBI33/graph.json","fetch_events":"https://pith.science/api/pith-number/ATL65RN6RVHQ763FAYVMWEBI33/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ATL65RN6RVHQ763FAYVMWEBI33/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ATL65RN6RVHQ763FAYVMWEBI33/action/storage_attestation","attest_author":"https://pith.science/pith/ATL65RN6RVHQ763FAYVMWEBI33/action/author_attestation","sign_citation":"https://pith.science/pith/ATL65RN6RVHQ763FAYVMWEBI33/action/citation_signature","submit_replication":"https://pith.science/pith/ATL65RN6RVHQ763FAYVMWEBI33/action/replication_record"}},"created_at":"2026-05-18T01:19:31.553948+00:00","updated_at":"2026-05-18T01:19:31.553948+00:00"}