{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:HWJ5B2YH6OLE4SCHW6YB2FWVRR","short_pith_number":"pith:HWJ5B2YH","canonical_record":{"source":{"id":"1812.05319","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-13T09:05:11Z","cross_cats_sorted":[],"title_canon_sha256":"9a63dceefc845b195449b67ab3148ca21473e6cf51289cc6c39c00e0737e80ea","abstract_canon_sha256":"ef811144432641a812a9ac7a1ab0176f8c6c3fe7d111044c8cf6cb0f8a0666c0"},"schema_version":"1.0"},"canonical_sha256":"3d93d0eb07f3964e4847b7b01d16d58c593e1332e8ed34f85a262a71b18e014e","source":{"kind":"arxiv","id":"1812.05319","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.05319","created_at":"2026-05-17T23:58:23Z"},{"alias_kind":"arxiv_version","alias_value":"1812.05319v1","created_at":"2026-05-17T23:58:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.05319","created_at":"2026-05-17T23:58:23Z"},{"alias_kind":"pith_short_12","alias_value":"HWJ5B2YH6OLE","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HWJ5B2YH6OLE4SCH","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HWJ5B2YH","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:HWJ5B2YH6OLE4SCHW6YB2FWVRR","target":"record","payload":{"canonical_record":{"source":{"id":"1812.05319","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-13T09:05:11Z","cross_cats_sorted":[],"title_canon_sha256":"9a63dceefc845b195449b67ab3148ca21473e6cf51289cc6c39c00e0737e80ea","abstract_canon_sha256":"ef811144432641a812a9ac7a1ab0176f8c6c3fe7d111044c8cf6cb0f8a0666c0"},"schema_version":"1.0"},"canonical_sha256":"3d93d0eb07f3964e4847b7b01d16d58c593e1332e8ed34f85a262a71b18e014e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:23.795089Z","signature_b64":"/03m3CpNZl4V0rnXL/SBZHzGdg7cXq1L9mxcWLPeAIgJNhSzu/m93X4sKLcDKqNMIrp5cDDLmSz75P8bDVCAAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d93d0eb07f3964e4847b7b01d16d58c593e1332e8ed34f85a262a71b18e014e","last_reissued_at":"2026-05-17T23:58:23.794421Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:23.794421Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.05319","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":"2026-05-17T23:58:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AvNL5wTUvD6F2iQYX1T5+CTsSOvGZp5RklUHgyeSXfNrgyhhBnl5uo0khB4O6v2XctH+uvfBfMwoBoEikd12Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T14:50:12.739561Z"},"content_sha256":"b172217711e3f4ad58547f469641dbc91446378cf5f57d37bcb2f2b25888a3e8","schema_version":"1.0","event_id":"sha256:b172217711e3f4ad58547f469641dbc91446378cf5f57d37bcb2f2b25888a3e8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:HWJ5B2YH6OLE4SCHW6YB2FWVRR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Omni-directional Feature Learning for Person Re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"De-Shuang Huang, Di Wu, Hong-Wei Yang","submitted_at":"2018-12-13T09:05:11Z","abstract_excerpt":"Person re-identification (PReID) has received increasing attention due to it is an important part in intelligent surveillance. Recently, many state-of-the-art methods on PReID are part-based deep models. Most of them focus on learning the part feature representation of person body in horizontal direction. However, the feature representation of body in vertical direction is usually ignored. Besides, the spatial information between these part features and the different feature channels is not considered. In this study, we introduce a multi-branches deep model for PReID. Specifically, the model c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.05319","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":"2026-05-17T23:58:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aaBnvpDT4R+kU+rvy99456AbM1rh1nH6FjsgdTeWSiu7kUwP5m1gU3WbRkMJ3XkgIT7tr67FFYUUqA0hOWJuCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T14:50:12.740165Z"},"content_sha256":"9ab20f5b53f052587285d91655534c4cc5de716c0624bd940921c9712aba7bf1","schema_version":"1.0","event_id":"sha256:9ab20f5b53f052587285d91655534c4cc5de716c0624bd940921c9712aba7bf1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HWJ5B2YH6OLE4SCHW6YB2FWVRR/bundle.json","state_url":"https://pith.science/pith/HWJ5B2YH6OLE4SCHW6YB2FWVRR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HWJ5B2YH6OLE4SCHW6YB2FWVRR/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":"2026-05-24T14:50:12Z","links":{"resolver":"https://pith.science/pith/HWJ5B2YH6OLE4SCHW6YB2FWVRR","bundle":"https://pith.science/pith/HWJ5B2YH6OLE4SCHW6YB2FWVRR/bundle.json","state":"https://pith.science/pith/HWJ5B2YH6OLE4SCHW6YB2FWVRR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HWJ5B2YH6OLE4SCHW6YB2FWVRR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:HWJ5B2YH6OLE4SCHW6YB2FWVRR","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":"ef811144432641a812a9ac7a1ab0176f8c6c3fe7d111044c8cf6cb0f8a0666c0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-13T09:05:11Z","title_canon_sha256":"9a63dceefc845b195449b67ab3148ca21473e6cf51289cc6c39c00e0737e80ea"},"schema_version":"1.0","source":{"id":"1812.05319","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.05319","created_at":"2026-05-17T23:58:23Z"},{"alias_kind":"arxiv_version","alias_value":"1812.05319v1","created_at":"2026-05-17T23:58:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.05319","created_at":"2026-05-17T23:58:23Z"},{"alias_kind":"pith_short_12","alias_value":"HWJ5B2YH6OLE","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HWJ5B2YH6OLE4SCH","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HWJ5B2YH","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:9ab20f5b53f052587285d91655534c4cc5de716c0624bd940921c9712aba7bf1","target":"graph","created_at":"2026-05-17T23:58:23Z","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":"Person re-identification (PReID) has received increasing attention due to it is an important part in intelligent surveillance. Recently, many state-of-the-art methods on PReID are part-based deep models. Most of them focus on learning the part feature representation of person body in horizontal direction. However, the feature representation of body in vertical direction is usually ignored. Besides, the spatial information between these part features and the different feature channels is not considered. In this study, we introduce a multi-branches deep model for PReID. Specifically, the model c","authors_text":"De-Shuang Huang, Di Wu, Hong-Wei Yang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-13T09:05:11Z","title":"Omni-directional Feature Learning for Person Re-identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.05319","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:b172217711e3f4ad58547f469641dbc91446378cf5f57d37bcb2f2b25888a3e8","target":"record","created_at":"2026-05-17T23:58:23Z","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":"ef811144432641a812a9ac7a1ab0176f8c6c3fe7d111044c8cf6cb0f8a0666c0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-13T09:05:11Z","title_canon_sha256":"9a63dceefc845b195449b67ab3148ca21473e6cf51289cc6c39c00e0737e80ea"},"schema_version":"1.0","source":{"id":"1812.05319","kind":"arxiv","version":1}},"canonical_sha256":"3d93d0eb07f3964e4847b7b01d16d58c593e1332e8ed34f85a262a71b18e014e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3d93d0eb07f3964e4847b7b01d16d58c593e1332e8ed34f85a262a71b18e014e","first_computed_at":"2026-05-17T23:58:23.794421Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:58:23.794421Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/03m3CpNZl4V0rnXL/SBZHzGdg7cXq1L9mxcWLPeAIgJNhSzu/m93X4sKLcDKqNMIrp5cDDLmSz75P8bDVCAAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:58:23.795089Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.05319","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b172217711e3f4ad58547f469641dbc91446378cf5f57d37bcb2f2b25888a3e8","sha256:9ab20f5b53f052587285d91655534c4cc5de716c0624bd940921c9712aba7bf1"],"state_sha256":"b3552f8c24286a519799b2f609c125f6fd8a24389556d62cd468455d3d2789d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LcRk11FM4jrixBq2RDERtMSyfWYo8J6dstWYbXQoVaePQW3nwgxLiNK9zpWxxqGH8Xc0ZgrvdQra5mwBHBFMAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T14:50:12.744628Z","bundle_sha256":"2b4af827520127403ed26037bbc1e21055e2a25c611fc28a76911655df2c945e"}}