{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:57ZK2NFOXGSQT4ORPTCBBYOS3P","short_pith_number":"pith:57ZK2NFO","schema_version":"1.0","canonical_sha256":"eff2ad34aeb9a509f1d17cc410e1d2dbf6c4a59cd7c3a84758f6e7767caa671d","source":{"kind":"arxiv","id":"2501.15045","version":2},"attestation_state":"computed","paper":{"title":"Towards Robust Unsupervised Attention Prediction in Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Huadong Ma, Mengshi Qi, Pengfei Zhu, Xiaoyang Bi","submitted_at":"2025-01-25T03:01:26Z","abstract_excerpt":"Robustly predicting attention regions of interest for self-driving systems is crucial for driving safety but presents significant challenges due to the labor-intensive nature of obtaining large-scale attention labels and the domain gap between self-driving scenarios and natural scenes. These challenges are further exacerbated by complex traffic environments, including camera corruption under adverse weather, noise interferences, and central bias from long-tail distributions. To address these issues, we propose a robust unsupervised attention prediction method. An Uncertainty Mining Branch refi"},"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":"2501.15045","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-25T03:01:26Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"674ee7a620f3ee133385b614712c6c8f3206df6afd0d77f4dad076cadafb64b7","abstract_canon_sha256":"beeb602b4997a1c71d07a33a9bb39dbea70c7ad63c48dfac81f4b856694c4ba5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:06:47.677999Z","signature_b64":"yKtuTUn6QZcT6pR7oqS6Jap5mrct12xKKm67PqrrpsDhYdepMuqynBRNFp9ffbg52prHSHv7K/kO7krp+cINDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eff2ad34aeb9a509f1d17cc410e1d2dbf6c4a59cd7c3a84758f6e7767caa671d","last_reissued_at":"2026-07-05T10:06:47.677463Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:06:47.677463Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Robust Unsupervised Attention Prediction in Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Huadong Ma, Mengshi Qi, Pengfei Zhu, Xiaoyang Bi","submitted_at":"2025-01-25T03:01:26Z","abstract_excerpt":"Robustly predicting attention regions of interest for self-driving systems is crucial for driving safety but presents significant challenges due to the labor-intensive nature of obtaining large-scale attention labels and the domain gap between self-driving scenarios and natural scenes. These challenges are further exacerbated by complex traffic environments, including camera corruption under adverse weather, noise interferences, and central bias from long-tail distributions. To address these issues, we propose a robust unsupervised attention prediction method. An Uncertainty Mining Branch refi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.15045","kind":"arxiv","version":2},"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/2501.15045/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":"2501.15045","created_at":"2026-07-05T10:06:47.677523+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.15045v2","created_at":"2026-07-05T10:06:47.677523+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.15045","created_at":"2026-07-05T10:06:47.677523+00:00"},{"alias_kind":"pith_short_12","alias_value":"57ZK2NFOXGSQ","created_at":"2026-07-05T10:06:47.677523+00:00"},{"alias_kind":"pith_short_16","alias_value":"57ZK2NFOXGSQT4OR","created_at":"2026-07-05T10:06:47.677523+00:00"},{"alias_kind":"pith_short_8","alias_value":"57ZK2NFO","created_at":"2026-07-05T10:06:47.677523+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/57ZK2NFOXGSQT4ORPTCBBYOS3P","json":"https://pith.science/pith/57ZK2NFOXGSQT4ORPTCBBYOS3P.json","graph_json":"https://pith.science/api/pith-number/57ZK2NFOXGSQT4ORPTCBBYOS3P/graph.json","events_json":"https://pith.science/api/pith-number/57ZK2NFOXGSQT4ORPTCBBYOS3P/events.json","paper":"https://pith.science/paper/57ZK2NFO"},"agent_actions":{"view_html":"https://pith.science/pith/57ZK2NFOXGSQT4ORPTCBBYOS3P","download_json":"https://pith.science/pith/57ZK2NFOXGSQT4ORPTCBBYOS3P.json","view_paper":"https://pith.science/paper/57ZK2NFO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.15045&json=true","fetch_graph":"https://pith.science/api/pith-number/57ZK2NFOXGSQT4ORPTCBBYOS3P/graph.json","fetch_events":"https://pith.science/api/pith-number/57ZK2NFOXGSQT4ORPTCBBYOS3P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/57ZK2NFOXGSQT4ORPTCBBYOS3P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/57ZK2NFOXGSQT4ORPTCBBYOS3P/action/storage_attestation","attest_author":"https://pith.science/pith/57ZK2NFOXGSQT4ORPTCBBYOS3P/action/author_attestation","sign_citation":"https://pith.science/pith/57ZK2NFOXGSQT4ORPTCBBYOS3P/action/citation_signature","submit_replication":"https://pith.science/pith/57ZK2NFOXGSQT4ORPTCBBYOS3P/action/replication_record"}},"created_at":"2026-07-05T10:06:47.677523+00:00","updated_at":"2026-07-05T10:06:47.677523+00:00"}