{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:AXZVR42IQH3A763AH2WHDTUBWR","short_pith_number":"pith:AXZVR42I","canonical_record":{"source":{"id":"2306.04650","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-06T07:24:53Z","cross_cats_sorted":[],"title_canon_sha256":"848cdb0bde81a97589d7aff6b95795c0ed740d7c4420dfc0b8bf9f8aebc2de7d","abstract_canon_sha256":"f28565550b7651b062179cc504e328ce69b1475508f6e6820202b21af21310d9"},"schema_version":"1.0"},"canonical_sha256":"05f358f34881f60ffb603eac71ce81b45f970d2d4f02911e9aba1158126dbb00","source":{"kind":"arxiv","id":"2306.04650","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.04650","created_at":"2026-07-05T06:18:40Z"},{"alias_kind":"arxiv_version","alias_value":"2306.04650v1","created_at":"2026-07-05T06:18:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.04650","created_at":"2026-07-05T06:18:40Z"},{"alias_kind":"pith_short_12","alias_value":"AXZVR42IQH3A","created_at":"2026-07-05T06:18:40Z"},{"alias_kind":"pith_short_16","alias_value":"AXZVR42IQH3A763A","created_at":"2026-07-05T06:18:40Z"},{"alias_kind":"pith_short_8","alias_value":"AXZVR42I","created_at":"2026-07-05T06:18:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:AXZVR42IQH3A763AH2WHDTUBWR","target":"record","payload":{"canonical_record":{"source":{"id":"2306.04650","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-06T07:24:53Z","cross_cats_sorted":[],"title_canon_sha256":"848cdb0bde81a97589d7aff6b95795c0ed740d7c4420dfc0b8bf9f8aebc2de7d","abstract_canon_sha256":"f28565550b7651b062179cc504e328ce69b1475508f6e6820202b21af21310d9"},"schema_version":"1.0"},"canonical_sha256":"05f358f34881f60ffb603eac71ce81b45f970d2d4f02911e9aba1158126dbb00","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:18:40.213386Z","signature_b64":"Y+bbjNR428FPDw3oGE8hfCiOEm6oJtQ4SROjzd1VFRDu16VAmawu4oJs12l+Z0Pw4tDw6bQB2YQqrEj2DsLkCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05f358f34881f60ffb603eac71ce81b45f970d2d4f02911e9aba1158126dbb00","last_reissued_at":"2026-07-05T06:18:40.213004Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:18:40.213004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.04650","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:18:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YWIcik0q+VkPn2ASsW8E9520cCQZYZltJ8X5LAJ0Zjl8GfaWcVy15lVASqaHDgLN14Dsbrsnb7ZtMW/2EWa5DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T23:04:41.854316Z"},"content_sha256":"ca7b75278a9ecf27fa4dff02cd9287ad6f42ee45b3a4006d6583bc503efbb1e2","schema_version":"1.0","event_id":"sha256:ca7b75278a9ecf27fa4dff02cd9287ad6f42ee45b3a4006d6583bc503efbb1e2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:AXZVR42IQH3A763AH2WHDTUBWR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GaitMPL: Gait Recognition with Memory-Augmented Progressive Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Huanzhang Dou, Lin Dong, Pengyi Zhang, Xi Li, Yuhan Zhao, Zequn Qin","submitted_at":"2023-06-06T07:24:53Z","abstract_excerpt":"Gait recognition aims at identifying the pedestrians at a long distance by their biometric gait patterns. It is inherently challenging due to the various covariates and the properties of silhouettes (textureless and colorless), which result in two kinds of pair-wise hard samples: the same pedestrian could have distinct silhouettes (intra-class diversity) and different pedestrians could have similar silhouettes (inter-class similarity). In this work, we propose to solve the hard sample issue with a Memory-augmented Progressive Learning network (GaitMPL), including Dynamic Reweighting Progressiv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.04650","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2306.04650/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:18:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SInJC+3vAqQ+lUPfJn6dYT5sq+ScIT1ZvYM41+WRy8WZ8ypmhyE26cpC7S93In2VDujROUUsFjWBLrH0gy/uBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T23:04:41.855238Z"},"content_sha256":"721264f55351ec1a9b33766c76f58493f76311a786d00533b65f42175b26b178","schema_version":"1.0","event_id":"sha256:721264f55351ec1a9b33766c76f58493f76311a786d00533b65f42175b26b178"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AXZVR42IQH3A763AH2WHDTUBWR/bundle.json","state_url":"https://pith.science/pith/AXZVR42IQH3A763AH2WHDTUBWR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AXZVR42IQH3A763AH2WHDTUBWR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T23:04:41Z","links":{"resolver":"https://pith.science/pith/AXZVR42IQH3A763AH2WHDTUBWR","bundle":"https://pith.science/pith/AXZVR42IQH3A763AH2WHDTUBWR/bundle.json","state":"https://pith.science/pith/AXZVR42IQH3A763AH2WHDTUBWR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AXZVR42IQH3A763AH2WHDTUBWR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:AXZVR42IQH3A763AH2WHDTUBWR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f28565550b7651b062179cc504e328ce69b1475508f6e6820202b21af21310d9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-06T07:24:53Z","title_canon_sha256":"848cdb0bde81a97589d7aff6b95795c0ed740d7c4420dfc0b8bf9f8aebc2de7d"},"schema_version":"1.0","source":{"id":"2306.04650","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.04650","created_at":"2026-07-05T06:18:40Z"},{"alias_kind":"arxiv_version","alias_value":"2306.04650v1","created_at":"2026-07-05T06:18:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.04650","created_at":"2026-07-05T06:18:40Z"},{"alias_kind":"pith_short_12","alias_value":"AXZVR42IQH3A","created_at":"2026-07-05T06:18:40Z"},{"alias_kind":"pith_short_16","alias_value":"AXZVR42IQH3A763A","created_at":"2026-07-05T06:18:40Z"},{"alias_kind":"pith_short_8","alias_value":"AXZVR42I","created_at":"2026-07-05T06:18:40Z"}],"graph_snapshots":[{"event_id":"sha256:721264f55351ec1a9b33766c76f58493f76311a786d00533b65f42175b26b178","target":"graph","created_at":"2026-07-05T06:18:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2306.04650/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Gait recognition aims at identifying the pedestrians at a long distance by their biometric gait patterns. It is inherently challenging due to the various covariates and the properties of silhouettes (textureless and colorless), which result in two kinds of pair-wise hard samples: the same pedestrian could have distinct silhouettes (intra-class diversity) and different pedestrians could have similar silhouettes (inter-class similarity). In this work, we propose to solve the hard sample issue with a Memory-augmented Progressive Learning network (GaitMPL), including Dynamic Reweighting Progressiv","authors_text":"Huanzhang Dou, Lin Dong, Pengyi Zhang, Xi Li, Yuhan Zhao, Zequn Qin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-06T07:24:53Z","title":"GaitMPL: Gait Recognition with Memory-Augmented Progressive Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.04650","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ca7b75278a9ecf27fa4dff02cd9287ad6f42ee45b3a4006d6583bc503efbb1e2","target":"record","created_at":"2026-07-05T06:18:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f28565550b7651b062179cc504e328ce69b1475508f6e6820202b21af21310d9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-06T07:24:53Z","title_canon_sha256":"848cdb0bde81a97589d7aff6b95795c0ed740d7c4420dfc0b8bf9f8aebc2de7d"},"schema_version":"1.0","source":{"id":"2306.04650","kind":"arxiv","version":1}},"canonical_sha256":"05f358f34881f60ffb603eac71ce81b45f970d2d4f02911e9aba1158126dbb00","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"05f358f34881f60ffb603eac71ce81b45f970d2d4f02911e9aba1158126dbb00","first_computed_at":"2026-07-05T06:18:40.213004Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:18:40.213004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Y+bbjNR428FPDw3oGE8hfCiOEm6oJtQ4SROjzd1VFRDu16VAmawu4oJs12l+Z0Pw4tDw6bQB2YQqrEj2DsLkCg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:18:40.213386Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.04650","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ca7b75278a9ecf27fa4dff02cd9287ad6f42ee45b3a4006d6583bc503efbb1e2","sha256:721264f55351ec1a9b33766c76f58493f76311a786d00533b65f42175b26b178"],"state_sha256":"e8aa14021840f9062468cda490a55658dd2ea8a63d9d87e0d6eeec69ac8a15c7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pEDhHTX+01GLr9Gf9Ps1q94yviXr13YqtUxuf8Mz9IucDEYvaWL9ay7/3ArrS9qcp+rNmu/5X0HNDvVm8LhyDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T23:04:41.861284Z","bundle_sha256":"138921af46ced35964d0d64e420d04123091365ea0f467bef51d503ab9546f55"}}