{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:N5AY6PDPMGVC3HSSRKU7H4IFPL","short_pith_number":"pith:N5AY6PDP","canonical_record":{"source":{"id":"1811.02074","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-05T23:00:15Z","cross_cats_sorted":[],"title_canon_sha256":"70a2a36328b8e9469d6e3e8533ad9b858c1d194b4b687a01c62adb45333b10f6","abstract_canon_sha256":"5272748b232772b03685ae469d21ad431b972a20efa78b647aa57e334ad6eb56"},"schema_version":"1.0"},"canonical_sha256":"6f418f3c6f61aa2d9e528aa9f3f1057ae482e3909ddc0c4ca9e3df5055a8c840","source":{"kind":"arxiv","id":"1811.02074","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.02074","created_at":"2026-05-18T00:01:25Z"},{"alias_kind":"arxiv_version","alias_value":"1811.02074v1","created_at":"2026-05-18T00:01:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.02074","created_at":"2026-05-18T00:01:25Z"},{"alias_kind":"pith_short_12","alias_value":"N5AY6PDPMGVC","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"N5AY6PDPMGVC3HSS","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"N5AY6PDP","created_at":"2026-05-18T12:32:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:N5AY6PDPMGVC3HSSRKU7H4IFPL","target":"record","payload":{"canonical_record":{"source":{"id":"1811.02074","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-05T23:00:15Z","cross_cats_sorted":[],"title_canon_sha256":"70a2a36328b8e9469d6e3e8533ad9b858c1d194b4b687a01c62adb45333b10f6","abstract_canon_sha256":"5272748b232772b03685ae469d21ad431b972a20efa78b647aa57e334ad6eb56"},"schema_version":"1.0"},"canonical_sha256":"6f418f3c6f61aa2d9e528aa9f3f1057ae482e3909ddc0c4ca9e3df5055a8c840","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:25.109462Z","signature_b64":"qWa28U59yV4UhWIlCFkjdhGQoKCoCKuJZLsBOUPeHu5nRMj2ElaDxpaPQ7qAiVuZhphE3gKwXiujeAhjH6amDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f418f3c6f61aa2d9e528aa9f3f1057ae482e3909ddc0c4ca9e3df5055a8c840","last_reissued_at":"2026-05-18T00:01:25.109103Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:25.109103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.02074","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":"2026-05-18T00:01:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9Cq46S8SLAfvDEbTxvyH1/QZ2TL+/OICQOolOC/Rz7EUaUfmUxze0Df7nWTlVPoqgZcQI/04oHG8K1T44FFMDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T14:46:44.072079Z"},"content_sha256":"0356c739d5b62b6d2e9be48113f72d4d1dedb566d8ed01ba1a85b20828a9d65f","schema_version":"1.0","event_id":"sha256:0356c739d5b62b6d2e9be48113f72d4d1dedb566d8ed01ba1a85b20828a9d65f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:N5AY6PDPMGVC3HSSRKU7H4IFPL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Leveraging Virtual and Real Person for Unsupervised Person Re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fengxiang Yang, Shaozi Li, Sheng Lian, Zhiming Luo, Zhun Zhong","submitted_at":"2018-11-05T23:00:15Z","abstract_excerpt":"Person re-identification (re-ID) is a challenging problem especially when no labels are available for training. Although recent deep re-ID methods have achieved great improvement, it is still difficult to optimize deep re-ID model without annotations in training data. To address this problem, this study introduces a novel approach for unsupervised person re-ID by leveraging virtual and real data. Our approach includes two components: virtual person generation and training of deep re-ID model. For virtual person generation, we learn a person generation model and a camera style transfer model us"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.02074","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":"2026-05-18T00:01:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dyrEqef8KaitZO2MPdKV2j+URywcfPw4oS2MbOv2NFVRyeH+KXqQHgQI/5NUNNel6Mwi9v9R6DFE2o5r3ac7Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T14:46:44.072755Z"},"content_sha256":"892709d7cc480b32b1228542a19f9be5bd8c6bd92c47f4deb1ad599afc746326","schema_version":"1.0","event_id":"sha256:892709d7cc480b32b1228542a19f9be5bd8c6bd92c47f4deb1ad599afc746326"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N5AY6PDPMGVC3HSSRKU7H4IFPL/bundle.json","state_url":"https://pith.science/pith/N5AY6PDPMGVC3HSSRKU7H4IFPL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N5AY6PDPMGVC3HSSRKU7H4IFPL/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":"2026-05-26T14:46:44Z","links":{"resolver":"https://pith.science/pith/N5AY6PDPMGVC3HSSRKU7H4IFPL","bundle":"https://pith.science/pith/N5AY6PDPMGVC3HSSRKU7H4IFPL/bundle.json","state":"https://pith.science/pith/N5AY6PDPMGVC3HSSRKU7H4IFPL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N5AY6PDPMGVC3HSSRKU7H4IFPL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:N5AY6PDPMGVC3HSSRKU7H4IFPL","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":"5272748b232772b03685ae469d21ad431b972a20efa78b647aa57e334ad6eb56","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-05T23:00:15Z","title_canon_sha256":"70a2a36328b8e9469d6e3e8533ad9b858c1d194b4b687a01c62adb45333b10f6"},"schema_version":"1.0","source":{"id":"1811.02074","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.02074","created_at":"2026-05-18T00:01:25Z"},{"alias_kind":"arxiv_version","alias_value":"1811.02074v1","created_at":"2026-05-18T00:01:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.02074","created_at":"2026-05-18T00:01:25Z"},{"alias_kind":"pith_short_12","alias_value":"N5AY6PDPMGVC","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"N5AY6PDPMGVC3HSS","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"N5AY6PDP","created_at":"2026-05-18T12:32:40Z"}],"graph_snapshots":[{"event_id":"sha256:892709d7cc480b32b1228542a19f9be5bd8c6bd92c47f4deb1ad599afc746326","target":"graph","created_at":"2026-05-18T00:01:25Z","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 (re-ID) is a challenging problem especially when no labels are available for training. Although recent deep re-ID methods have achieved great improvement, it is still difficult to optimize deep re-ID model without annotations in training data. To address this problem, this study introduces a novel approach for unsupervised person re-ID by leveraging virtual and real data. Our approach includes two components: virtual person generation and training of deep re-ID model. For virtual person generation, we learn a person generation model and a camera style transfer model us","authors_text":"Fengxiang Yang, Shaozi Li, Sheng Lian, Zhiming Luo, Zhun Zhong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-05T23:00:15Z","title":"Leveraging Virtual and Real Person for Unsupervised Person Re-identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.02074","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":"sha256:0356c739d5b62b6d2e9be48113f72d4d1dedb566d8ed01ba1a85b20828a9d65f","target":"record","created_at":"2026-05-18T00:01:25Z","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":"5272748b232772b03685ae469d21ad431b972a20efa78b647aa57e334ad6eb56","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-05T23:00:15Z","title_canon_sha256":"70a2a36328b8e9469d6e3e8533ad9b858c1d194b4b687a01c62adb45333b10f6"},"schema_version":"1.0","source":{"id":"1811.02074","kind":"arxiv","version":1}},"canonical_sha256":"6f418f3c6f61aa2d9e528aa9f3f1057ae482e3909ddc0c4ca9e3df5055a8c840","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6f418f3c6f61aa2d9e528aa9f3f1057ae482e3909ddc0c4ca9e3df5055a8c840","first_computed_at":"2026-05-18T00:01:25.109103Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:01:25.109103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qWa28U59yV4UhWIlCFkjdhGQoKCoCKuJZLsBOUPeHu5nRMj2ElaDxpaPQ7qAiVuZhphE3gKwXiujeAhjH6amDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:01:25.109462Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.02074","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0356c739d5b62b6d2e9be48113f72d4d1dedb566d8ed01ba1a85b20828a9d65f","sha256:892709d7cc480b32b1228542a19f9be5bd8c6bd92c47f4deb1ad599afc746326"],"state_sha256":"c590df9589bd2a894dbd1048e39e37ce6e878184cdde24cd6e16cb41db863912"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LL8o7gH4YdGKyXUnyYoufmcYj3BMhY3MkiuLcA9fQ06NW2ZymgZGGNUxZh9RTXb4+axezM9pY+HIc07BomycDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T14:46:44.076216Z","bundle_sha256":"c6c2d345b958203911a71bcf55b12092c20bcf71d8f52fe6aa77548eac620486"}}