{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HARGMKT43YKSWOU5VG67QCCR42","short_pith_number":"pith:HARGMKT4","schema_version":"1.0","canonical_sha256":"3822662a7cde152b3a9da9bdf80851e6b321071d9daf896c2642c60145480a17","source":{"kind":"arxiv","id":"2501.17977","version":1},"attestation_state":"computed","paper":{"title":"TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.CV","authors_text":"Lei Cheng, Siyang Cao","submitted_at":"2025-01-29T20:21:41Z","abstract_excerpt":"Despite significant advancements in environment perception capabilities for autonomous driving and intelligent robotics, cameras and LiDARs remain notoriously unreliable in low-light conditions and adverse weather, which limits their effectiveness. Radar serves as a reliable and low-cost sensor that can effectively complement these limitations. However, radar-based object detection has been underexplored due to the inherent weaknesses of radar data, such as low resolution, high noise, and lack of visual information. In this paper, we present TransRAD, a novel 3D radar object detection model de"},"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":"2501.17977","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-29T20:21:41Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"58b1a380f3d411761cb6181a81b18b5656f6dc9c505fd818af63b7cf16129db5","abstract_canon_sha256":"5d9080b95a9c6fe086baa0e4017e4cdb0bd23c9aaeae965d1ed7f57e812d4202"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:07:05.180435Z","signature_b64":"NVmstAUP6ApUn6SeNj4jH8mPeGFcTJpdM+bjVfP4SzqMFGZxTJDi8ME7dmaHxBu6V7Vjh1HoZNCvOo6eyEuyBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3822662a7cde152b3a9da9bdf80851e6b321071d9daf896c2642c60145480a17","last_reissued_at":"2026-07-05T10:07:05.179874Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:07:05.179874Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.CV","authors_text":"Lei Cheng, Siyang Cao","submitted_at":"2025-01-29T20:21:41Z","abstract_excerpt":"Despite significant advancements in environment perception capabilities for autonomous driving and intelligent robotics, cameras and LiDARs remain notoriously unreliable in low-light conditions and adverse weather, which limits their effectiveness. Radar serves as a reliable and low-cost sensor that can effectively complement these limitations. However, radar-based object detection has been underexplored due to the inherent weaknesses of radar data, such as low resolution, high noise, and lack of visual information. In this paper, we present TransRAD, a novel 3D radar object detection model de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.17977","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/2501.17977/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":"2501.17977","created_at":"2026-07-05T10:07:05.179934+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.17977v1","created_at":"2026-07-05T10:07:05.179934+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.17977","created_at":"2026-07-05T10:07:05.179934+00:00"},{"alias_kind":"pith_short_12","alias_value":"HARGMKT43YKS","created_at":"2026-07-05T10:07:05.179934+00:00"},{"alias_kind":"pith_short_16","alias_value":"HARGMKT43YKSWOU5","created_at":"2026-07-05T10:07:05.179934+00:00"},{"alias_kind":"pith_short_8","alias_value":"HARGMKT4","created_at":"2026-07-05T10:07:05.179934+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/HARGMKT43YKSWOU5VG67QCCR42","json":"https://pith.science/pith/HARGMKT43YKSWOU5VG67QCCR42.json","graph_json":"https://pith.science/api/pith-number/HARGMKT43YKSWOU5VG67QCCR42/graph.json","events_json":"https://pith.science/api/pith-number/HARGMKT43YKSWOU5VG67QCCR42/events.json","paper":"https://pith.science/paper/HARGMKT4"},"agent_actions":{"view_html":"https://pith.science/pith/HARGMKT43YKSWOU5VG67QCCR42","download_json":"https://pith.science/pith/HARGMKT43YKSWOU5VG67QCCR42.json","view_paper":"https://pith.science/paper/HARGMKT4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.17977&json=true","fetch_graph":"https://pith.science/api/pith-number/HARGMKT43YKSWOU5VG67QCCR42/graph.json","fetch_events":"https://pith.science/api/pith-number/HARGMKT43YKSWOU5VG67QCCR42/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HARGMKT43YKSWOU5VG67QCCR42/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HARGMKT43YKSWOU5VG67QCCR42/action/storage_attestation","attest_author":"https://pith.science/pith/HARGMKT43YKSWOU5VG67QCCR42/action/author_attestation","sign_citation":"https://pith.science/pith/HARGMKT43YKSWOU5VG67QCCR42/action/citation_signature","submit_replication":"https://pith.science/pith/HARGMKT43YKSWOU5VG67QCCR42/action/replication_record"}},"created_at":"2026-07-05T10:07:05.179934+00:00","updated_at":"2026-07-05T10:07:05.179934+00:00"}