{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:5CBVPV4QWLG2S6CAS5DX2FKUXQ","short_pith_number":"pith:5CBVPV4Q","schema_version":"1.0","canonical_sha256":"e88357d790b2cda9784097477d1554bc2b36c7ef84509ef0aeeda444fa8c6b02","source":{"kind":"arxiv","id":"2111.04086","version":1},"attestation_state":"computed","paper":{"title":"Meta Cross-Modal Hashing on Long-Tailed Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DB","cs.MA"],"primary_cat":"cs.LG","authors_text":"Carlotta Domeniconi, Guoxian Yu, Runmin Wang, Xiangliang Zhang","submitted_at":"2021-11-07T13:31:16Z","abstract_excerpt":"Due to the advantage of reducing storage while speeding up query time on big heterogeneous data, cross-modal hashing has been extensively studied for approximate nearest neighbor search of multi-modal data. Most hashing methods assume that training data is class-balanced.However, in practice, real world data often have a long-tailed distribution. In this paper, we introduce a meta-learning based cross-modal hashing method (MetaCMH) to handle long-tailed data. Due to the lack of training samples in the tail classes, MetaCMH first learns direct features from data in different modalities, and the"},"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":"2111.04086","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-07T13:31:16Z","cross_cats_sorted":["cs.AI","cs.DB","cs.MA"],"title_canon_sha256":"e758ec6c693d98b2e352738c0e03007e66335ce084ed51c0da5280aa4344c208","abstract_canon_sha256":"d425fdc561ed4b4bce1f644717f948b4422d94c8d9e1443aa2be649f025cfc73"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:29:55.509536Z","signature_b64":"uJ7DvUVVyRsph6jEFbi8d63NciYWvfOrcnUFbiZOR2VqRS05S6mrBNKbgttP/00WxxOk+4ZePJ6C8XnSC0xABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e88357d790b2cda9784097477d1554bc2b36c7ef84509ef0aeeda444fa8c6b02","last_reissued_at":"2026-07-05T03:29:55.509116Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:29:55.509116Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Meta Cross-Modal Hashing on Long-Tailed Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DB","cs.MA"],"primary_cat":"cs.LG","authors_text":"Carlotta Domeniconi, Guoxian Yu, Runmin Wang, Xiangliang Zhang","submitted_at":"2021-11-07T13:31:16Z","abstract_excerpt":"Due to the advantage of reducing storage while speeding up query time on big heterogeneous data, cross-modal hashing has been extensively studied for approximate nearest neighbor search of multi-modal data. Most hashing methods assume that training data is class-balanced.However, in practice, real world data often have a long-tailed distribution. In this paper, we introduce a meta-learning based cross-modal hashing method (MetaCMH) to handle long-tailed data. Due to the lack of training samples in the tail classes, MetaCMH first learns direct features from data in different modalities, and the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.04086","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/2111.04086/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":"2111.04086","created_at":"2026-07-05T03:29:55.509174+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.04086v1","created_at":"2026-07-05T03:29:55.509174+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.04086","created_at":"2026-07-05T03:29:55.509174+00:00"},{"alias_kind":"pith_short_12","alias_value":"5CBVPV4QWLG2","created_at":"2026-07-05T03:29:55.509174+00:00"},{"alias_kind":"pith_short_16","alias_value":"5CBVPV4QWLG2S6CA","created_at":"2026-07-05T03:29:55.509174+00:00"},{"alias_kind":"pith_short_8","alias_value":"5CBVPV4Q","created_at":"2026-07-05T03:29:55.509174+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/5CBVPV4QWLG2S6CAS5DX2FKUXQ","json":"https://pith.science/pith/5CBVPV4QWLG2S6CAS5DX2FKUXQ.json","graph_json":"https://pith.science/api/pith-number/5CBVPV4QWLG2S6CAS5DX2FKUXQ/graph.json","events_json":"https://pith.science/api/pith-number/5CBVPV4QWLG2S6CAS5DX2FKUXQ/events.json","paper":"https://pith.science/paper/5CBVPV4Q"},"agent_actions":{"view_html":"https://pith.science/pith/5CBVPV4QWLG2S6CAS5DX2FKUXQ","download_json":"https://pith.science/pith/5CBVPV4QWLG2S6CAS5DX2FKUXQ.json","view_paper":"https://pith.science/paper/5CBVPV4Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.04086&json=true","fetch_graph":"https://pith.science/api/pith-number/5CBVPV4QWLG2S6CAS5DX2FKUXQ/graph.json","fetch_events":"https://pith.science/api/pith-number/5CBVPV4QWLG2S6CAS5DX2FKUXQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5CBVPV4QWLG2S6CAS5DX2FKUXQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5CBVPV4QWLG2S6CAS5DX2FKUXQ/action/storage_attestation","attest_author":"https://pith.science/pith/5CBVPV4QWLG2S6CAS5DX2FKUXQ/action/author_attestation","sign_citation":"https://pith.science/pith/5CBVPV4QWLG2S6CAS5DX2FKUXQ/action/citation_signature","submit_replication":"https://pith.science/pith/5CBVPV4QWLG2S6CAS5DX2FKUXQ/action/replication_record"}},"created_at":"2026-07-05T03:29:55.509174+00:00","updated_at":"2026-07-05T03:29:55.509174+00:00"}