{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2CBNKJ6NPG7W7PU6JRF4VFZCZ2","short_pith_number":"pith:2CBNKJ6N","schema_version":"1.0","canonical_sha256":"d082d527cd79bf6fbe9e4c4bca9722cea460302e12c09b0537c0f4a0395249f0","source":{"kind":"arxiv","id":"2508.10397","version":1},"attestation_state":"computed","paper":{"title":"PQ-DAF: Pose-driven Quality-controlled Data Augmentation for Data-scarce Driver Distraction Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Haibin Sun, Xinghui Song","submitted_at":"2025-08-14T06:54:28Z","abstract_excerpt":"Driver distraction detection is essential for improving traffic safety and reducing road accidents. However, existing models often suffer from degraded generalization when deployed in real-world scenarios. This limitation primarily arises from the few-shot learning challenge caused by the high cost of data annotation in practical environments, as well as the substantial domain shift between training datasets and target deployment conditions. To address these issues, we propose a Pose-driven Quality-controlled Data Augmentation Framework (PQ-DAF) that leverages a vision-language model for sampl"},"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":"2508.10397","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-08-14T06:54:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ff88eaff797e2eb5dd5dce46efafe9305c5c54825d8e79b0603fee91b603bad9","abstract_canon_sha256":"b141734bd7347852f48cfb04257e1d0a5976efe2fce657b859254d1d8d77b363"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:55.331291Z","signature_b64":"mGidvMHpgGrBC4UAEHcJ52yMXC/MiGJuuxL4JbKSiHyFoitAm1cvgkkPq7XcKdFN0NLS1BgmVirOnyWAZXpUAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d082d527cd79bf6fbe9e4c4bca9722cea460302e12c09b0537c0f4a0395249f0","last_reissued_at":"2026-07-05T11:53:55.330806Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:55.330806Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PQ-DAF: Pose-driven Quality-controlled Data Augmentation for Data-scarce Driver Distraction Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Haibin Sun, Xinghui Song","submitted_at":"2025-08-14T06:54:28Z","abstract_excerpt":"Driver distraction detection is essential for improving traffic safety and reducing road accidents. However, existing models often suffer from degraded generalization when deployed in real-world scenarios. This limitation primarily arises from the few-shot learning challenge caused by the high cost of data annotation in practical environments, as well as the substantial domain shift between training datasets and target deployment conditions. To address these issues, we propose a Pose-driven Quality-controlled Data Augmentation Framework (PQ-DAF) that leverages a vision-language model for sampl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.10397","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/2508.10397/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":"2508.10397","created_at":"2026-07-05T11:53:55.330866+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.10397v1","created_at":"2026-07-05T11:53:55.330866+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.10397","created_at":"2026-07-05T11:53:55.330866+00:00"},{"alias_kind":"pith_short_12","alias_value":"2CBNKJ6NPG7W","created_at":"2026-07-05T11:53:55.330866+00:00"},{"alias_kind":"pith_short_16","alias_value":"2CBNKJ6NPG7W7PU6","created_at":"2026-07-05T11:53:55.330866+00:00"},{"alias_kind":"pith_short_8","alias_value":"2CBNKJ6N","created_at":"2026-07-05T11:53:55.330866+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/2CBNKJ6NPG7W7PU6JRF4VFZCZ2","json":"https://pith.science/pith/2CBNKJ6NPG7W7PU6JRF4VFZCZ2.json","graph_json":"https://pith.science/api/pith-number/2CBNKJ6NPG7W7PU6JRF4VFZCZ2/graph.json","events_json":"https://pith.science/api/pith-number/2CBNKJ6NPG7W7PU6JRF4VFZCZ2/events.json","paper":"https://pith.science/paper/2CBNKJ6N"},"agent_actions":{"view_html":"https://pith.science/pith/2CBNKJ6NPG7W7PU6JRF4VFZCZ2","download_json":"https://pith.science/pith/2CBNKJ6NPG7W7PU6JRF4VFZCZ2.json","view_paper":"https://pith.science/paper/2CBNKJ6N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.10397&json=true","fetch_graph":"https://pith.science/api/pith-number/2CBNKJ6NPG7W7PU6JRF4VFZCZ2/graph.json","fetch_events":"https://pith.science/api/pith-number/2CBNKJ6NPG7W7PU6JRF4VFZCZ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2CBNKJ6NPG7W7PU6JRF4VFZCZ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2CBNKJ6NPG7W7PU6JRF4VFZCZ2/action/storage_attestation","attest_author":"https://pith.science/pith/2CBNKJ6NPG7W7PU6JRF4VFZCZ2/action/author_attestation","sign_citation":"https://pith.science/pith/2CBNKJ6NPG7W7PU6JRF4VFZCZ2/action/citation_signature","submit_replication":"https://pith.science/pith/2CBNKJ6NPG7W7PU6JRF4VFZCZ2/action/replication_record"}},"created_at":"2026-07-05T11:53:55.330866+00:00","updated_at":"2026-07-05T11:53:55.330866+00:00"}