{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KAIZVFHPJYNLGCL4HREB2XTOJN","short_pith_number":"pith:KAIZVFHP","schema_version":"1.0","canonical_sha256":"50119a94ef4e1ab3097c3c481d5e6e4b75707d5de91377686d6c4edb323b9f7a","source":{"kind":"arxiv","id":"2403.00724","version":1},"attestation_state":"computed","paper":{"title":"Few-Shot Relation Extraction with Hybrid Visual Evidence","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.CL","authors_text":"Hoda Eldardiry, Jiaying Gong","submitted_at":"2024-03-01T18:20:11Z","abstract_excerpt":"The goal of few-shot relation extraction is to predict relations between name entities in a sentence when only a few labeled instances are available for training. Existing few-shot relation extraction methods focus on uni-modal information such as text only. This reduces performance when there are no clear contexts between the name entities described in text. We propose a multi-modal few-shot relation extraction model (MFS-HVE) that leverages both textual and visual semantic information to learn a multi-modal representation jointly. The MFS-HVE includes semantic feature extractors and multi-mo"},"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":"2403.00724","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-01T18:20:11Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"9f4dd2d76b2115f630ef556b12039fd1aca82982530fe33d3911f362cfa24020","abstract_canon_sha256":"4751f30f340aa687b17e6c54ef606c4241b91fd23443b0d7bfbba1dac38f4f54"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:51:07.697038Z","signature_b64":"8QPytrDhh47aHgkHvYiwlEgVW2NfAArxVknzYuFUz0ou/Qh6oYmqtl7tpRRCY1pw3EiyvA43bDv1saUOKltcBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50119a94ef4e1ab3097c3c481d5e6e4b75707d5de91377686d6c4edb323b9f7a","last_reissued_at":"2026-07-05T07:51:07.696601Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:51:07.696601Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Few-Shot Relation Extraction with Hybrid Visual Evidence","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.CL","authors_text":"Hoda Eldardiry, Jiaying Gong","submitted_at":"2024-03-01T18:20:11Z","abstract_excerpt":"The goal of few-shot relation extraction is to predict relations between name entities in a sentence when only a few labeled instances are available for training. Existing few-shot relation extraction methods focus on uni-modal information such as text only. This reduces performance when there are no clear contexts between the name entities described in text. We propose a multi-modal few-shot relation extraction model (MFS-HVE) that leverages both textual and visual semantic information to learn a multi-modal representation jointly. The MFS-HVE includes semantic feature extractors and multi-mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.00724","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/2403.00724/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":"2403.00724","created_at":"2026-07-05T07:51:07.696657+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.00724v1","created_at":"2026-07-05T07:51:07.696657+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.00724","created_at":"2026-07-05T07:51:07.696657+00:00"},{"alias_kind":"pith_short_12","alias_value":"KAIZVFHPJYNL","created_at":"2026-07-05T07:51:07.696657+00:00"},{"alias_kind":"pith_short_16","alias_value":"KAIZVFHPJYNLGCL4","created_at":"2026-07-05T07:51:07.696657+00:00"},{"alias_kind":"pith_short_8","alias_value":"KAIZVFHP","created_at":"2026-07-05T07:51:07.696657+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/KAIZVFHPJYNLGCL4HREB2XTOJN","json":"https://pith.science/pith/KAIZVFHPJYNLGCL4HREB2XTOJN.json","graph_json":"https://pith.science/api/pith-number/KAIZVFHPJYNLGCL4HREB2XTOJN/graph.json","events_json":"https://pith.science/api/pith-number/KAIZVFHPJYNLGCL4HREB2XTOJN/events.json","paper":"https://pith.science/paper/KAIZVFHP"},"agent_actions":{"view_html":"https://pith.science/pith/KAIZVFHPJYNLGCL4HREB2XTOJN","download_json":"https://pith.science/pith/KAIZVFHPJYNLGCL4HREB2XTOJN.json","view_paper":"https://pith.science/paper/KAIZVFHP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.00724&json=true","fetch_graph":"https://pith.science/api/pith-number/KAIZVFHPJYNLGCL4HREB2XTOJN/graph.json","fetch_events":"https://pith.science/api/pith-number/KAIZVFHPJYNLGCL4HREB2XTOJN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KAIZVFHPJYNLGCL4HREB2XTOJN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KAIZVFHPJYNLGCL4HREB2XTOJN/action/storage_attestation","attest_author":"https://pith.science/pith/KAIZVFHPJYNLGCL4HREB2XTOJN/action/author_attestation","sign_citation":"https://pith.science/pith/KAIZVFHPJYNLGCL4HREB2XTOJN/action/citation_signature","submit_replication":"https://pith.science/pith/KAIZVFHPJYNLGCL4HREB2XTOJN/action/replication_record"}},"created_at":"2026-07-05T07:51:07.696657+00:00","updated_at":"2026-07-05T07:51:07.696657+00:00"}