{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:FGGZJI22CXZTUNNXDSCBV5KHKW","short_pith_number":"pith:FGGZJI22","schema_version":"1.0","canonical_sha256":"298d94a35a15f33a35b71c841af54755b2512f2af0d2547aa88d6c8653d65e63","source":{"kind":"arxiv","id":"2007.02388","version":1},"attestation_state":"computed","paper":{"title":"Learning Color Compatibility in Fashion Outfits","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"C.-C. Jay Kuo, Chi-Hao Wu, Heming Zhang, Jianchao Tan, Jue Wang, Xuewen Yang","submitted_at":"2020-07-05T17:09:31Z","abstract_excerpt":"Color compatibility is important for evaluating the compatibility of a fashion outfit, yet it was neglected in previous studies. We bring this important problem to researchers' attention and present a compatibility learning framework as solution to various fashion tasks. The framework consists of a novel way to model outfit compatibility and an innovative learning scheme. Specifically, we model the outfits as graphs and propose a novel graph construction to better utilize the power of graph neural networks. Then we utilize both ground-truth labels and pseudo labels to train the compatibility m"},"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":"2007.02388","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-05T17:09:31Z","cross_cats_sorted":[],"title_canon_sha256":"d58efc2545fe727c4ce12c73b255edb34bd934706d6da35c0483bee272245eb9","abstract_canon_sha256":"56e2723f96d0a379dcdf1c48bc11987d2801f5c7e66d495282d998cc250ef070"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:16:21.853737Z","signature_b64":"3+HeWBzjs46gfHL4/NM0VjVzc9cmCr08V3JC//xTJR60fYza9G+Y5nCSQbe9tqM6tMEIXLTSJgI/Ce3Q2nvfDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"298d94a35a15f33a35b71c841af54755b2512f2af0d2547aa88d6c8653d65e63","last_reissued_at":"2026-07-05T01:16:21.853177Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:16:21.853177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Color Compatibility in Fashion Outfits","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"C.-C. Jay Kuo, Chi-Hao Wu, Heming Zhang, Jianchao Tan, Jue Wang, Xuewen Yang","submitted_at":"2020-07-05T17:09:31Z","abstract_excerpt":"Color compatibility is important for evaluating the compatibility of a fashion outfit, yet it was neglected in previous studies. We bring this important problem to researchers' attention and present a compatibility learning framework as solution to various fashion tasks. The framework consists of a novel way to model outfit compatibility and an innovative learning scheme. Specifically, we model the outfits as graphs and propose a novel graph construction to better utilize the power of graph neural networks. Then we utilize both ground-truth labels and pseudo labels to train the compatibility m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.02388","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/2007.02388/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":"2007.02388","created_at":"2026-07-05T01:16:21.853237+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.02388v1","created_at":"2026-07-05T01:16:21.853237+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.02388","created_at":"2026-07-05T01:16:21.853237+00:00"},{"alias_kind":"pith_short_12","alias_value":"FGGZJI22CXZT","created_at":"2026-07-05T01:16:21.853237+00:00"},{"alias_kind":"pith_short_16","alias_value":"FGGZJI22CXZTUNNX","created_at":"2026-07-05T01:16:21.853237+00:00"},{"alias_kind":"pith_short_8","alias_value":"FGGZJI22","created_at":"2026-07-05T01:16:21.853237+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.11105","citing_title":"Hybrid-Hierarchical Fashion Graph Attention Network for Compatibility-Oriented and Personalized Outfit Recommendation","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FGGZJI22CXZTUNNXDSCBV5KHKW","json":"https://pith.science/pith/FGGZJI22CXZTUNNXDSCBV5KHKW.json","graph_json":"https://pith.science/api/pith-number/FGGZJI22CXZTUNNXDSCBV5KHKW/graph.json","events_json":"https://pith.science/api/pith-number/FGGZJI22CXZTUNNXDSCBV5KHKW/events.json","paper":"https://pith.science/paper/FGGZJI22"},"agent_actions":{"view_html":"https://pith.science/pith/FGGZJI22CXZTUNNXDSCBV5KHKW","download_json":"https://pith.science/pith/FGGZJI22CXZTUNNXDSCBV5KHKW.json","view_paper":"https://pith.science/paper/FGGZJI22","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.02388&json=true","fetch_graph":"https://pith.science/api/pith-number/FGGZJI22CXZTUNNXDSCBV5KHKW/graph.json","fetch_events":"https://pith.science/api/pith-number/FGGZJI22CXZTUNNXDSCBV5KHKW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FGGZJI22CXZTUNNXDSCBV5KHKW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FGGZJI22CXZTUNNXDSCBV5KHKW/action/storage_attestation","attest_author":"https://pith.science/pith/FGGZJI22CXZTUNNXDSCBV5KHKW/action/author_attestation","sign_citation":"https://pith.science/pith/FGGZJI22CXZTUNNXDSCBV5KHKW/action/citation_signature","submit_replication":"https://pith.science/pith/FGGZJI22CXZTUNNXDSCBV5KHKW/action/replication_record"}},"created_at":"2026-07-05T01:16:21.853237+00:00","updated_at":"2026-07-05T01:16:21.853237+00:00"}