{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:ITLHQDB2TMVNOXKZVRY5U5LQH5","short_pith_number":"pith:ITLHQDB2","schema_version":"1.0","canonical_sha256":"44d6780c3a9b2ad75d59ac71da75703f5e32cd93507f98c8eec8ce1dbf24f425","source":{"kind":"arxiv","id":"2111.04080","version":1},"attestation_state":"computed","paper":{"title":"Cross-modal Zero-shot Hashing by Label Attributes Embedding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.MM"],"primary_cat":"cs.CV","authors_text":"Carlotta Domeniconi, Guoxian Yu, Lei Liu, Lizhen Cui, Runmin Wang, Xiangliang Zhang","submitted_at":"2021-11-07T13:19:05Z","abstract_excerpt":"Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search. Most CMH solutions ideally assume the labels of training and testing set are identical. However, the assumption is often violated, causing a zero-shot CMH problem. Recent efforts to address this issue focus on transferring knowledge from the seen classes to the unseen ones using label attributes. However, the attributes are isolated from the features of multi-modal data. To reduce the information gap, we introduce an approach called LAEH (Label Attributes Embedding for zero-shot c"},"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":"2111.04080","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-11-07T13:19:05Z","cross_cats_sorted":["cs.AI","cs.LG","cs.MM"],"title_canon_sha256":"3f48fdf3b4c891b4dc831f1d42ccfe30263d6cc92068a0ca8cd630fc083bbc9a","abstract_canon_sha256":"0b08c992af9e904ccd14191096c61e688add4ec480f9b63bdcdaa1570bac3f63"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:29:55.450594Z","signature_b64":"VpUO1LbibU/lrPt84NwlE4VHE0LgcBFHObeWxcKazNRIFbYQaiRbGttYh+3o8PY1djrZYRnzfC6IvT4LrLkuDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44d6780c3a9b2ad75d59ac71da75703f5e32cd93507f98c8eec8ce1dbf24f425","last_reissued_at":"2026-07-05T03:29:55.450170Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:29:55.450170Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cross-modal Zero-shot Hashing by Label Attributes Embedding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.MM"],"primary_cat":"cs.CV","authors_text":"Carlotta Domeniconi, Guoxian Yu, Lei Liu, Lizhen Cui, Runmin Wang, Xiangliang Zhang","submitted_at":"2021-11-07T13:19:05Z","abstract_excerpt":"Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search. Most CMH solutions ideally assume the labels of training and testing set are identical. However, the assumption is often violated, causing a zero-shot CMH problem. Recent efforts to address this issue focus on transferring knowledge from the seen classes to the unseen ones using label attributes. However, the attributes are isolated from the features of multi-modal data. To reduce the information gap, we introduce an approach called LAEH (Label Attributes Embedding for zero-shot c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.04080","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/2111.04080/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":"2111.04080","created_at":"2026-07-05T03:29:55.450231+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.04080v1","created_at":"2026-07-05T03:29:55.450231+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.04080","created_at":"2026-07-05T03:29:55.450231+00:00"},{"alias_kind":"pith_short_12","alias_value":"ITLHQDB2TMVN","created_at":"2026-07-05T03:29:55.450231+00:00"},{"alias_kind":"pith_short_16","alias_value":"ITLHQDB2TMVNOXKZ","created_at":"2026-07-05T03:29:55.450231+00:00"},{"alias_kind":"pith_short_8","alias_value":"ITLHQDB2","created_at":"2026-07-05T03:29:55.450231+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/ITLHQDB2TMVNOXKZVRY5U5LQH5","json":"https://pith.science/pith/ITLHQDB2TMVNOXKZVRY5U5LQH5.json","graph_json":"https://pith.science/api/pith-number/ITLHQDB2TMVNOXKZVRY5U5LQH5/graph.json","events_json":"https://pith.science/api/pith-number/ITLHQDB2TMVNOXKZVRY5U5LQH5/events.json","paper":"https://pith.science/paper/ITLHQDB2"},"agent_actions":{"view_html":"https://pith.science/pith/ITLHQDB2TMVNOXKZVRY5U5LQH5","download_json":"https://pith.science/pith/ITLHQDB2TMVNOXKZVRY5U5LQH5.json","view_paper":"https://pith.science/paper/ITLHQDB2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.04080&json=true","fetch_graph":"https://pith.science/api/pith-number/ITLHQDB2TMVNOXKZVRY5U5LQH5/graph.json","fetch_events":"https://pith.science/api/pith-number/ITLHQDB2TMVNOXKZVRY5U5LQH5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ITLHQDB2TMVNOXKZVRY5U5LQH5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ITLHQDB2TMVNOXKZVRY5U5LQH5/action/storage_attestation","attest_author":"https://pith.science/pith/ITLHQDB2TMVNOXKZVRY5U5LQH5/action/author_attestation","sign_citation":"https://pith.science/pith/ITLHQDB2TMVNOXKZVRY5U5LQH5/action/citation_signature","submit_replication":"https://pith.science/pith/ITLHQDB2TMVNOXKZVRY5U5LQH5/action/replication_record"}},"created_at":"2026-07-05T03:29:55.450231+00:00","updated_at":"2026-07-05T03:29:55.450231+00:00"}