{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YI5VCPROEYWH7F5BLANMBWGGZW","short_pith_number":"pith:YI5VCPRO","schema_version":"1.0","canonical_sha256":"c23b513e2e262c7f97a1581ac0d8c6cd9f4fbe2b5dfab6fe92e34701afa64916","source":{"kind":"arxiv","id":"2405.04164","version":1},"attestation_state":"computed","paper":{"title":"Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Necati Cihan Camgoz, Richard Bowden, Ryan Wong","submitted_at":"2024-05-07T10:00:38Z","abstract_excerpt":"Automatic Sign Language Translation requires the integration of both computer vision and natural language processing to effectively bridge the communication gap between sign and spoken languages. However, the deficiency in large-scale training data to support sign language translation means we need to leverage resources from spoken language. We introduce, Sign2GPT, a novel framework for sign language translation that utilizes large-scale pretrained vision and language models via lightweight adapters for gloss-free sign language translation. The lightweight adapters are crucial for sign languag"},"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":"2405.04164","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-07T10:00:38Z","cross_cats_sorted":[],"title_canon_sha256":"21b926ebfcfafc89a8de059b78fa336117c77b61924ae3de260b4f83c9904110","abstract_canon_sha256":"cd5ff212dd517c6a86e940e20fe05956314ad0851508994705c05bc669006563"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:16:32.687616Z","signature_b64":"xoHYmAc2YY0+cqSUeCMAFZSwZ+zgdNCOfxm1O+Kq8JyqfT/Z/J29yFn+akytSvoASFAfwnlieJfvn2uhBi7WBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c23b513e2e262c7f97a1581ac0d8c6cd9f4fbe2b5dfab6fe92e34701afa64916","last_reissued_at":"2026-07-05T08:16:32.687158Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:16:32.687158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Necati Cihan Camgoz, Richard Bowden, Ryan Wong","submitted_at":"2024-05-07T10:00:38Z","abstract_excerpt":"Automatic Sign Language Translation requires the integration of both computer vision and natural language processing to effectively bridge the communication gap between sign and spoken languages. However, the deficiency in large-scale training data to support sign language translation means we need to leverage resources from spoken language. We introduce, Sign2GPT, a novel framework for sign language translation that utilizes large-scale pretrained vision and language models via lightweight adapters for gloss-free sign language translation. The lightweight adapters are crucial for sign languag"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.04164","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/2405.04164/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":"2405.04164","created_at":"2026-07-05T08:16:32.687216+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.04164v1","created_at":"2026-07-05T08:16:32.687216+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.04164","created_at":"2026-07-05T08:16:32.687216+00:00"},{"alias_kind":"pith_short_12","alias_value":"YI5VCPROEYWH","created_at":"2026-07-05T08:16:32.687216+00:00"},{"alias_kind":"pith_short_16","alias_value":"YI5VCPROEYWH7F5B","created_at":"2026-07-05T08:16:32.687216+00:00"},{"alias_kind":"pith_short_8","alias_value":"YI5VCPRO","created_at":"2026-07-05T08:16:32.687216+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11925","citing_title":"Corpus Augmentation for Sign Language Translation via LLM-Guided Video Stitching","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03788","citing_title":"SLU-2K: A Question-Based Benchmark for Semantic Evaluation of Sign Language Translation","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14705","citing_title":"Towards Continuous Sign Language Conversation from Isolated Signs","ref_index":84,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22374","citing_title":"Selective Contrastive Learning For Gloss Free Sign Language Translation","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YI5VCPROEYWH7F5BLANMBWGGZW","json":"https://pith.science/pith/YI5VCPROEYWH7F5BLANMBWGGZW.json","graph_json":"https://pith.science/api/pith-number/YI5VCPROEYWH7F5BLANMBWGGZW/graph.json","events_json":"https://pith.science/api/pith-number/YI5VCPROEYWH7F5BLANMBWGGZW/events.json","paper":"https://pith.science/paper/YI5VCPRO"},"agent_actions":{"view_html":"https://pith.science/pith/YI5VCPROEYWH7F5BLANMBWGGZW","download_json":"https://pith.science/pith/YI5VCPROEYWH7F5BLANMBWGGZW.json","view_paper":"https://pith.science/paper/YI5VCPRO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.04164&json=true","fetch_graph":"https://pith.science/api/pith-number/YI5VCPROEYWH7F5BLANMBWGGZW/graph.json","fetch_events":"https://pith.science/api/pith-number/YI5VCPROEYWH7F5BLANMBWGGZW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YI5VCPROEYWH7F5BLANMBWGGZW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YI5VCPROEYWH7F5BLANMBWGGZW/action/storage_attestation","attest_author":"https://pith.science/pith/YI5VCPROEYWH7F5BLANMBWGGZW/action/author_attestation","sign_citation":"https://pith.science/pith/YI5VCPROEYWH7F5BLANMBWGGZW/action/citation_signature","submit_replication":"https://pith.science/pith/YI5VCPROEYWH7F5BLANMBWGGZW/action/replication_record"}},"created_at":"2026-07-05T08:16:32.687216+00:00","updated_at":"2026-07-05T08:16:32.687216+00:00"}