{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:R4GHQGMPERIPOPILXZGVAR76DY","short_pith_number":"pith:R4GHQGMP","schema_version":"1.0","canonical_sha256":"8f0c78198f2450f73d0bbe4d5047fe1e0d13f798d15dadf1955e9063170edb41","source":{"kind":"arxiv","id":"2303.05234","version":2},"attestation_state":"computed","paper":{"title":"GPGait: Generalized Pose-based Gait Recognition","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Saihui Hou, Shibei Meng, Xuecai Hu, Yang Fu, Yongzhen Huang","submitted_at":"2023-03-09T13:17:13Z","abstract_excerpt":"Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods. However, the generalization ability of pose-based methods on different datasets is undesirably inferior to that of silhouette-based ones, which has received little attention but hinders the application of these methods in real-world scenarios. To improve the generalization ability of pose-based methods across datasets, we propose a \\textbf{G}eneralized \\textbf{P}ose-based \\textbf{Gait} recognition (\\textbf{GPGait}) framework. Fi"},"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":"2303.05234","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-09T13:17:13Z","cross_cats_sorted":[],"title_canon_sha256":"194b47d17c4f9b619d4cb87109679203e97e56c9555b811db676f41ce1beddd5","abstract_canon_sha256":"8eadb197bfb929e6208ea293d2eb4e93bb1d04035df58b16ee0f8f0110e26f7b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:41:13.580486Z","signature_b64":"3VmFc7sBlIEXBxtLYB4Bn3slhtznGjwoKBRQTWcCJkadegAXF4jyGkmQwShwgr0iqAv6xtN2g0DxDlEHbOgDAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f0c78198f2450f73d0bbe4d5047fe1e0d13f798d15dadf1955e9063170edb41","last_reissued_at":"2026-07-05T06:41:13.579984Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:41:13.579984Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GPGait: Generalized Pose-based Gait Recognition","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Saihui Hou, Shibei Meng, Xuecai Hu, Yang Fu, Yongzhen Huang","submitted_at":"2023-03-09T13:17:13Z","abstract_excerpt":"Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods. However, the generalization ability of pose-based methods on different datasets is undesirably inferior to that of silhouette-based ones, which has received little attention but hinders the application of these methods in real-world scenarios. To improve the generalization ability of pose-based methods across datasets, we propose a \\textbf{G}eneralized \\textbf{P}ose-based \\textbf{Gait} recognition (\\textbf{GPGait}) framework. Fi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.05234","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/2303.05234/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":"2303.05234","created_at":"2026-07-05T06:41:13.580037+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.05234v2","created_at":"2026-07-05T06:41:13.580037+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.05234","created_at":"2026-07-05T06:41:13.580037+00:00"},{"alias_kind":"pith_short_12","alias_value":"R4GHQGMPERIP","created_at":"2026-07-05T06:41:13.580037+00:00"},{"alias_kind":"pith_short_16","alias_value":"R4GHQGMPERIPOPIL","created_at":"2026-07-05T06:41:13.580037+00:00"},{"alias_kind":"pith_short_8","alias_value":"R4GHQGMP","created_at":"2026-07-05T06:41:13.580037+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.22139","citing_title":"EventGait: Towards Robust Gait Recognition with Event Streams","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R4GHQGMPERIPOPILXZGVAR76DY","json":"https://pith.science/pith/R4GHQGMPERIPOPILXZGVAR76DY.json","graph_json":"https://pith.science/api/pith-number/R4GHQGMPERIPOPILXZGVAR76DY/graph.json","events_json":"https://pith.science/api/pith-number/R4GHQGMPERIPOPILXZGVAR76DY/events.json","paper":"https://pith.science/paper/R4GHQGMP"},"agent_actions":{"view_html":"https://pith.science/pith/R4GHQGMPERIPOPILXZGVAR76DY","download_json":"https://pith.science/pith/R4GHQGMPERIPOPILXZGVAR76DY.json","view_paper":"https://pith.science/paper/R4GHQGMP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.05234&json=true","fetch_graph":"https://pith.science/api/pith-number/R4GHQGMPERIPOPILXZGVAR76DY/graph.json","fetch_events":"https://pith.science/api/pith-number/R4GHQGMPERIPOPILXZGVAR76DY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R4GHQGMPERIPOPILXZGVAR76DY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R4GHQGMPERIPOPILXZGVAR76DY/action/storage_attestation","attest_author":"https://pith.science/pith/R4GHQGMPERIPOPILXZGVAR76DY/action/author_attestation","sign_citation":"https://pith.science/pith/R4GHQGMPERIPOPILXZGVAR76DY/action/citation_signature","submit_replication":"https://pith.science/pith/R4GHQGMPERIPOPILXZGVAR76DY/action/replication_record"}},"created_at":"2026-07-05T06:41:13.580037+00:00","updated_at":"2026-07-05T06:41:13.580037+00:00"}