{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:4CNKE27I36CSPIZ2BYEO2ZH2EA","short_pith_number":"pith:4CNKE27I","schema_version":"1.0","canonical_sha256":"e09aa26be8df8527a33a0e08ed64fa203bad6e4ec1e698e1a0301d81fb278fb8","source":{"kind":"arxiv","id":"1903.12395","version":3},"attestation_state":"computed","paper":{"title":"Few-Shot Deep Adversarial Learning for Video-based Person Re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongzhi Yin, Ling Shao, Lin Wu, Meng Wang, Yang Wang","submitted_at":"2019-03-29T08:45:59Z","abstract_excerpt":"Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras. Also, it is noticed that learning effective video representations with view invariance is not explicitly addressed for which features exhibit different distributions otherwise. Th"},"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":"1903.12395","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-29T08:45:59Z","cross_cats_sorted":[],"title_canon_sha256":"ebba8f4c73ca7801907b0736792403b01a20e67777a58e6eb864d8e4d801b618","abstract_canon_sha256":"611382d0f74445f522461f4dcdf74109f0e5e650573bf22e619a80a6224e80a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:04:07.790982Z","signature_b64":"bLd7K/huRE7lF6vtnY8MF+sgwtfCqMW3lK5cS3J3gFuijaxXQSdtA763hVQltDPCK//xpMWU5TuIfWSEVNAjAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e09aa26be8df8527a33a0e08ed64fa203bad6e4ec1e698e1a0301d81fb278fb8","last_reissued_at":"2026-07-05T00:04:07.790572Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:04:07.790572Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Few-Shot Deep Adversarial Learning for Video-based Person Re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongzhi Yin, Ling Shao, Lin Wu, Meng Wang, Yang Wang","submitted_at":"2019-03-29T08:45:59Z","abstract_excerpt":"Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras. Also, it is noticed that learning effective video representations with view invariance is not explicitly addressed for which features exhibit different distributions otherwise. Th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.12395","kind":"arxiv","version":3},"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/1903.12395/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":"1903.12395","created_at":"2026-07-05T00:04:07.790629+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.12395v3","created_at":"2026-07-05T00:04:07.790629+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.12395","created_at":"2026-07-05T00:04:07.790629+00:00"},{"alias_kind":"pith_short_12","alias_value":"4CNKE27I36CS","created_at":"2026-07-05T00:04:07.790629+00:00"},{"alias_kind":"pith_short_16","alias_value":"4CNKE27I36CSPIZ2","created_at":"2026-07-05T00:04:07.790629+00:00"},{"alias_kind":"pith_short_8","alias_value":"4CNKE27I","created_at":"2026-07-05T00:04:07.790629+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/4CNKE27I36CSPIZ2BYEO2ZH2EA","json":"https://pith.science/pith/4CNKE27I36CSPIZ2BYEO2ZH2EA.json","graph_json":"https://pith.science/api/pith-number/4CNKE27I36CSPIZ2BYEO2ZH2EA/graph.json","events_json":"https://pith.science/api/pith-number/4CNKE27I36CSPIZ2BYEO2ZH2EA/events.json","paper":"https://pith.science/paper/4CNKE27I"},"agent_actions":{"view_html":"https://pith.science/pith/4CNKE27I36CSPIZ2BYEO2ZH2EA","download_json":"https://pith.science/pith/4CNKE27I36CSPIZ2BYEO2ZH2EA.json","view_paper":"https://pith.science/paper/4CNKE27I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.12395&json=true","fetch_graph":"https://pith.science/api/pith-number/4CNKE27I36CSPIZ2BYEO2ZH2EA/graph.json","fetch_events":"https://pith.science/api/pith-number/4CNKE27I36CSPIZ2BYEO2ZH2EA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4CNKE27I36CSPIZ2BYEO2ZH2EA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4CNKE27I36CSPIZ2BYEO2ZH2EA/action/storage_attestation","attest_author":"https://pith.science/pith/4CNKE27I36CSPIZ2BYEO2ZH2EA/action/author_attestation","sign_citation":"https://pith.science/pith/4CNKE27I36CSPIZ2BYEO2ZH2EA/action/citation_signature","submit_replication":"https://pith.science/pith/4CNKE27I36CSPIZ2BYEO2ZH2EA/action/replication_record"}},"created_at":"2026-07-05T00:04:07.790629+00:00","updated_at":"2026-07-05T00:04:07.790629+00:00"}