{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:NNZVFKDMU52EDRDOHUNH27MVSF","short_pith_number":"pith:NNZVFKDM","canonical_record":{"source":{"id":"1705.02145","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-05T09:24:13Z","cross_cats_sorted":[],"title_canon_sha256":"dde1d3668a9a29f7575fb4154bac06ac91d97780f20c91862c56707b075d2e24","abstract_canon_sha256":"5d3a27c24afa4778108d45390280da582c62da8d964d4876d1f2aa708ccca99f"},"schema_version":"1.0"},"canonical_sha256":"6b7352a86ca77441c46e3d1a7d7d959177a9d4e32ba36548889505a662f8e04e","source":{"kind":"arxiv","id":"1705.02145","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.02145","created_at":"2026-05-18T00:45:00Z"},{"alias_kind":"arxiv_version","alias_value":"1705.02145v1","created_at":"2026-05-18T00:45:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.02145","created_at":"2026-05-18T00:45:00Z"},{"alias_kind":"pith_short_12","alias_value":"NNZVFKDMU52E","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"NNZVFKDMU52EDRDO","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"NNZVFKDM","created_at":"2026-05-18T12:31:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:NNZVFKDMU52EDRDOHUNH27MVSF","target":"record","payload":{"canonical_record":{"source":{"id":"1705.02145","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-05T09:24:13Z","cross_cats_sorted":[],"title_canon_sha256":"dde1d3668a9a29f7575fb4154bac06ac91d97780f20c91862c56707b075d2e24","abstract_canon_sha256":"5d3a27c24afa4778108d45390280da582c62da8d964d4876d1f2aa708ccca99f"},"schema_version":"1.0"},"canonical_sha256":"6b7352a86ca77441c46e3d1a7d7d959177a9d4e32ba36548889505a662f8e04e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:00.553670Z","signature_b64":"gDbq5TaZ+SiO3Kk0SafgrnrUM4yOTUY9DHPhoTcDYZIlJnaFwLNS/v6FX4s8o5nGLciKXZrKNoe3EhAlTri7Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6b7352a86ca77441c46e3d1a7d7d959177a9d4e32ba36548889505a662f8e04e","last_reissued_at":"2026-05-18T00:45:00.553238Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:00.553238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.02145","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":"2026-05-18T00:45:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RBG+kRVlXulr7hnmElE0HR7wfKhUxkvOwyQCjsZ4tnRQqkB2HPloKuCSvcrj3xnPbKA0N/wd11auKyEXcsoxCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T15:12:30.465764Z"},"content_sha256":"6beaf321c7ffbf327e024a139b87878aba47c06404a63126c08a424773a57ef1","schema_version":"1.0","event_id":"sha256:6beaf321c7ffbf327e024a139b87878aba47c06404a63126c08a424773a57ef1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:NNZVFKDMU52EDRDOHUNH27MVSF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Part-based Deep Hashing for Large-scale Person Re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fuqing Zhu, Haiyan Fu, Liang Zheng, Qi Tian, Xiangwei Kong","submitted_at":"2017-05-05T09:24:13Z","abstract_excerpt":"Large-scale is a trend in person re-identification (re-id). It is important that real-time search be performed in a large gallery. While previous methods mostly focus on discriminative learning, this paper makes the attempt in integrating deep learning and hashing into one framework to evaluate the efficiency and accuracy for large-scale person re-id. We integrate spatial information for discriminative visual representation by partitioning the pedestrian image into horizontal parts. Specifically, Part-based Deep Hashing (PDH) is proposed, in which batches of triplet samples are employed as the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.02145","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:45:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fujtpZuWI1m/TEfvXNXa3oORE2aA+oO5TUoAqPKw5/wO5k4IP+fKF9ZlBLaLE8QjmDXkFw6q4TfwZ2I8cc+aCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T15:12:30.466492Z"},"content_sha256":"cf29025c989344f4fc29826256412dc264d04be0c1a4881113afa0517b41dee7","schema_version":"1.0","event_id":"sha256:cf29025c989344f4fc29826256412dc264d04be0c1a4881113afa0517b41dee7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NNZVFKDMU52EDRDOHUNH27MVSF/bundle.json","state_url":"https://pith.science/pith/NNZVFKDMU52EDRDOHUNH27MVSF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NNZVFKDMU52EDRDOHUNH27MVSF/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":"2026-05-30T15:12:30Z","links":{"resolver":"https://pith.science/pith/NNZVFKDMU52EDRDOHUNH27MVSF","bundle":"https://pith.science/pith/NNZVFKDMU52EDRDOHUNH27MVSF/bundle.json","state":"https://pith.science/pith/NNZVFKDMU52EDRDOHUNH27MVSF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NNZVFKDMU52EDRDOHUNH27MVSF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:NNZVFKDMU52EDRDOHUNH27MVSF","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":"5d3a27c24afa4778108d45390280da582c62da8d964d4876d1f2aa708ccca99f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-05T09:24:13Z","title_canon_sha256":"dde1d3668a9a29f7575fb4154bac06ac91d97780f20c91862c56707b075d2e24"},"schema_version":"1.0","source":{"id":"1705.02145","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.02145","created_at":"2026-05-18T00:45:00Z"},{"alias_kind":"arxiv_version","alias_value":"1705.02145v1","created_at":"2026-05-18T00:45:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.02145","created_at":"2026-05-18T00:45:00Z"},{"alias_kind":"pith_short_12","alias_value":"NNZVFKDMU52E","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"NNZVFKDMU52EDRDO","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"NNZVFKDM","created_at":"2026-05-18T12:31:34Z"}],"graph_snapshots":[{"event_id":"sha256:cf29025c989344f4fc29826256412dc264d04be0c1a4881113afa0517b41dee7","target":"graph","created_at":"2026-05-18T00:45:00Z","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"},"paper":{"abstract_excerpt":"Large-scale is a trend in person re-identification (re-id). It is important that real-time search be performed in a large gallery. While previous methods mostly focus on discriminative learning, this paper makes the attempt in integrating deep learning and hashing into one framework to evaluate the efficiency and accuracy for large-scale person re-id. We integrate spatial information for discriminative visual representation by partitioning the pedestrian image into horizontal parts. Specifically, Part-based Deep Hashing (PDH) is proposed, in which batches of triplet samples are employed as the","authors_text":"Fuqing Zhu, Haiyan Fu, Liang Zheng, Qi Tian, Xiangwei Kong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-05T09:24:13Z","title":"Part-based Deep Hashing for Large-scale Person Re-identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.02145","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":"sha256:6beaf321c7ffbf327e024a139b87878aba47c06404a63126c08a424773a57ef1","target":"record","created_at":"2026-05-18T00:45:00Z","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":"5d3a27c24afa4778108d45390280da582c62da8d964d4876d1f2aa708ccca99f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-05T09:24:13Z","title_canon_sha256":"dde1d3668a9a29f7575fb4154bac06ac91d97780f20c91862c56707b075d2e24"},"schema_version":"1.0","source":{"id":"1705.02145","kind":"arxiv","version":1}},"canonical_sha256":"6b7352a86ca77441c46e3d1a7d7d959177a9d4e32ba36548889505a662f8e04e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6b7352a86ca77441c46e3d1a7d7d959177a9d4e32ba36548889505a662f8e04e","first_computed_at":"2026-05-18T00:45:00.553238Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:45:00.553238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gDbq5TaZ+SiO3Kk0SafgrnrUM4yOTUY9DHPhoTcDYZIlJnaFwLNS/v6FX4s8o5nGLciKXZrKNoe3EhAlTri7Ag==","signature_status":"signed_v1","signed_at":"2026-05-18T00:45:00.553670Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.02145","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6beaf321c7ffbf327e024a139b87878aba47c06404a63126c08a424773a57ef1","sha256:cf29025c989344f4fc29826256412dc264d04be0c1a4881113afa0517b41dee7"],"state_sha256":"fe57b48a6150c92e6edc5d348583cd9b1087c55c365d87f3e36dc9e7428e4171"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hnrAt58oH0FEom24dNoJn7NRu2uf07uinqhA2cxfUOc3y8qwnksb2SrWv/078O0wLg5Gh6UUj8C4+c/DcOW1Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T15:12:30.470581Z","bundle_sha256":"ab477bd6bf2414d8800a123c9d8dd7571f512ac08a3878406de81141c937d8ec"}}