{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TWHW5HIECGHEAVM55I3WU7ZMEY","short_pith_number":"pith:TWHW5HIE","schema_version":"1.0","canonical_sha256":"9d8f6e9d04118e40559dea376a7f2c262d250046aafbface8399862094610d3e","source":{"kind":"arxiv","id":"2405.05672","version":1},"attestation_state":"computed","paper":{"title":"Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangkun Ma, Jiarui Liu, Mingzu Sun, Mo Guan, Yan Wang","submitted_at":"2024-05-09T10:58:37Z","abstract_excerpt":"Sign language serves as a non-vocal means of communication, transmitting information and significance through gestures, facial expressions, and bodily movements. The majority of current approaches for sign language recognition (SLR) and translation rely on RGB video inputs, which are vulnerable to fluctuations in the background. Employing a keypoint-based strategy not only mitigates the effects of background alterations but also substantially diminishes the computational demands of the model. Nevertheless, contemporary keypoint-based methodologies fail to fully harness the implicit knowledge e"},"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":"2405.05672","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-09T10:58:37Z","cross_cats_sorted":[],"title_canon_sha256":"87cf2181ce5c6d5bec45bd68d8f093285bac189c16b147917cefb7273ba3f6cf","abstract_canon_sha256":"4072632f97114a651c042f183f345819a1a56b8d16db7ac809bde1d7381ba703"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:17:21.421027Z","signature_b64":"ypaeHB4YLeL7mYJnrd5XB4rGReJOHuA7uTQFV6kagjuDwNM4SY/ZaDR84J1UHIaaY7+hfPdXwOX8a29Oj0ToBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d8f6e9d04118e40559dea376a7f2c262d250046aafbface8399862094610d3e","last_reissued_at":"2026-07-05T08:17:21.420583Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:17:21.420583Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangkun Ma, Jiarui Liu, Mingzu Sun, Mo Guan, Yan Wang","submitted_at":"2024-05-09T10:58:37Z","abstract_excerpt":"Sign language serves as a non-vocal means of communication, transmitting information and significance through gestures, facial expressions, and bodily movements. The majority of current approaches for sign language recognition (SLR) and translation rely on RGB video inputs, which are vulnerable to fluctuations in the background. Employing a keypoint-based strategy not only mitigates the effects of background alterations but also substantially diminishes the computational demands of the model. Nevertheless, contemporary keypoint-based methodologies fail to fully harness the implicit knowledge e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.05672","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/2405.05672/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":"2405.05672","created_at":"2026-07-05T08:17:21.420650+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.05672v1","created_at":"2026-07-05T08:17:21.420650+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.05672","created_at":"2026-07-05T08:17:21.420650+00:00"},{"alias_kind":"pith_short_12","alias_value":"TWHW5HIECGHE","created_at":"2026-07-05T08:17:21.420650+00:00"},{"alias_kind":"pith_short_16","alias_value":"TWHW5HIECGHEAVM5","created_at":"2026-07-05T08:17:21.420650+00:00"},{"alias_kind":"pith_short_8","alias_value":"TWHW5HIE","created_at":"2026-07-05T08:17:21.420650+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19352","citing_title":"Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards","ref_index":176,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TWHW5HIECGHEAVM55I3WU7ZMEY","json":"https://pith.science/pith/TWHW5HIECGHEAVM55I3WU7ZMEY.json","graph_json":"https://pith.science/api/pith-number/TWHW5HIECGHEAVM55I3WU7ZMEY/graph.json","events_json":"https://pith.science/api/pith-number/TWHW5HIECGHEAVM55I3WU7ZMEY/events.json","paper":"https://pith.science/paper/TWHW5HIE"},"agent_actions":{"view_html":"https://pith.science/pith/TWHW5HIECGHEAVM55I3WU7ZMEY","download_json":"https://pith.science/pith/TWHW5HIECGHEAVM55I3WU7ZMEY.json","view_paper":"https://pith.science/paper/TWHW5HIE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.05672&json=true","fetch_graph":"https://pith.science/api/pith-number/TWHW5HIECGHEAVM55I3WU7ZMEY/graph.json","fetch_events":"https://pith.science/api/pith-number/TWHW5HIECGHEAVM55I3WU7ZMEY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TWHW5HIECGHEAVM55I3WU7ZMEY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TWHW5HIECGHEAVM55I3WU7ZMEY/action/storage_attestation","attest_author":"https://pith.science/pith/TWHW5HIECGHEAVM55I3WU7ZMEY/action/author_attestation","sign_citation":"https://pith.science/pith/TWHW5HIECGHEAVM55I3WU7ZMEY/action/citation_signature","submit_replication":"https://pith.science/pith/TWHW5HIECGHEAVM55I3WU7ZMEY/action/replication_record"}},"created_at":"2026-07-05T08:17:21.420650+00:00","updated_at":"2026-07-05T08:17:21.420650+00:00"}