{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:SHBUEHC7XJPBYUOMPI4EHY7KBW","short_pith_number":"pith:SHBUEHC7","schema_version":"1.0","canonical_sha256":"91c3421c5fba5e1c51cc7a3843e3ea0db7b8239faed89ac5cfe6addb404a9ca0","source":{"kind":"arxiv","id":"2209.06209","version":1},"attestation_state":"computed","paper":{"title":"Look Before You Leap: Improving Text-based Person Retrieval by Learning A Consistent Cross-modal Common Manifold","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR","cs.MM"],"primary_cat":"cs.CV","authors_text":"Aichun Zhu, Chao Liu, Jingyi Xue, Tian Wang, Xili Wan, Yifeng Li, Zijie Wang","submitted_at":"2022-09-13T07:21:21Z","abstract_excerpt":"The core problem of text-based person retrieval is how to bridge the heterogeneous gap between multi-modal data. Many previous approaches contrive to learning a latent common manifold mapping paradigm following a \\textbf{cross-modal distribution consensus prediction (CDCP)} manner. When mapping features from distribution of one certain modality into the common manifold, feature distribution of the opposite modality is completely invisible. That is to say, how to achieve a cross-modal distribution consensus so as to embed and align the multi-modal features in a constructed cross-modal common ma"},"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":"2209.06209","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-13T07:21:21Z","cross_cats_sorted":["cs.IR","cs.MM"],"title_canon_sha256":"17f531e4cf1c221d3cb3e7e3026fa6c72d6d10463c28f29b52f6aaea292126b2","abstract_canon_sha256":"0411da1439e7d53e53b9959fec1f59d661ba04b763517b8035b944bf3db6df1a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:57:27.809397Z","signature_b64":"2FBeTFVx5/JLZjTnL8Ao2YafTLFsPhchTKO17Get74Snha637J18LLq7r6/gTzaZom5AuuHxKUziHvN8S8E3Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91c3421c5fba5e1c51cc7a3843e3ea0db7b8239faed89ac5cfe6addb404a9ca0","last_reissued_at":"2026-07-05T04:57:27.808993Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:57:27.808993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Look Before You Leap: Improving Text-based Person Retrieval by Learning A Consistent Cross-modal Common Manifold","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR","cs.MM"],"primary_cat":"cs.CV","authors_text":"Aichun Zhu, Chao Liu, Jingyi Xue, Tian Wang, Xili Wan, Yifeng Li, Zijie Wang","submitted_at":"2022-09-13T07:21:21Z","abstract_excerpt":"The core problem of text-based person retrieval is how to bridge the heterogeneous gap between multi-modal data. Many previous approaches contrive to learning a latent common manifold mapping paradigm following a \\textbf{cross-modal distribution consensus prediction (CDCP)} manner. When mapping features from distribution of one certain modality into the common manifold, feature distribution of the opposite modality is completely invisible. That is to say, how to achieve a cross-modal distribution consensus so as to embed and align the multi-modal features in a constructed cross-modal common ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.06209","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/2209.06209/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":"2209.06209","created_at":"2026-07-05T04:57:27.809047+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.06209v1","created_at":"2026-07-05T04:57:27.809047+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.06209","created_at":"2026-07-05T04:57:27.809047+00:00"},{"alias_kind":"pith_short_12","alias_value":"SHBUEHC7XJPB","created_at":"2026-07-05T04:57:27.809047+00:00"},{"alias_kind":"pith_short_16","alias_value":"SHBUEHC7XJPBYUOM","created_at":"2026-07-05T04:57:27.809047+00:00"},{"alias_kind":"pith_short_8","alias_value":"SHBUEHC7","created_at":"2026-07-05T04:57:27.809047+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SHBUEHC7XJPBYUOMPI4EHY7KBW","json":"https://pith.science/pith/SHBUEHC7XJPBYUOMPI4EHY7KBW.json","graph_json":"https://pith.science/api/pith-number/SHBUEHC7XJPBYUOMPI4EHY7KBW/graph.json","events_json":"https://pith.science/api/pith-number/SHBUEHC7XJPBYUOMPI4EHY7KBW/events.json","paper":"https://pith.science/paper/SHBUEHC7"},"agent_actions":{"view_html":"https://pith.science/pith/SHBUEHC7XJPBYUOMPI4EHY7KBW","download_json":"https://pith.science/pith/SHBUEHC7XJPBYUOMPI4EHY7KBW.json","view_paper":"https://pith.science/paper/SHBUEHC7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.06209&json=true","fetch_graph":"https://pith.science/api/pith-number/SHBUEHC7XJPBYUOMPI4EHY7KBW/graph.json","fetch_events":"https://pith.science/api/pith-number/SHBUEHC7XJPBYUOMPI4EHY7KBW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SHBUEHC7XJPBYUOMPI4EHY7KBW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SHBUEHC7XJPBYUOMPI4EHY7KBW/action/storage_attestation","attest_author":"https://pith.science/pith/SHBUEHC7XJPBYUOMPI4EHY7KBW/action/author_attestation","sign_citation":"https://pith.science/pith/SHBUEHC7XJPBYUOMPI4EHY7KBW/action/citation_signature","submit_replication":"https://pith.science/pith/SHBUEHC7XJPBYUOMPI4EHY7KBW/action/replication_record"}},"created_at":"2026-07-05T04:57:27.809047+00:00","updated_at":"2026-07-05T04:57:27.809047+00:00"}