{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:42BTH7YRSISQYSVT2RP7D5WYZU","short_pith_number":"pith:42BTH7YR","schema_version":"1.0","canonical_sha256":"e68333ff1192250c4ab3d45ff1f6d8cd0cd9abd8e8f8cc8b1976754d9622ef04","source":{"kind":"arxiv","id":"2303.04989","version":3},"attestation_state":"computed","paper":{"title":"ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Junchi Yan, Qingyun Li, Xue Yang, Ying Zeng, Yushi Chen","submitted_at":"2023-03-09T02:20:56Z","abstract_excerpt":"Existing oriented object detection methods commonly use metric AP$_{50}$ to measure the performance of the model. We argue that AP$_{50}$ is inherently unsuitable for oriented object detection due to its large tolerance in angle deviation. Therefore, we advocate using high-precision metric, e.g. AP$_{75}$, to measure the performance of models. In this paper, we propose an Aspect Ratio Sensitive Oriented Object Detector with Transformer, termed ARS-DETR, which exhibits a competitive performance in high-precision oriented object detection. Specifically, a new angle classification method, calling"},"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":"2303.04989","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-09T02:20:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a17aab8096d08ab05be4af2f8d82d99acdd341f38d783b08513f0809f50b3372","abstract_canon_sha256":"841007d74b6591d3e170b8924b78b8506c888101cd0de38d9674bcf74f3e41ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:04:56.638179Z","signature_b64":"g6moWi3CbVPujG0n6FdTB5kJcxCYya/P+W57sfgBnYBcUges0WXJcHH0aOsDeY36DSfqVguuv77qB90F2qc2BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e68333ff1192250c4ab3d45ff1f6d8cd0cd9abd8e8f8cc8b1976754d9622ef04","last_reissued_at":"2026-07-05T08:04:56.637782Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:04:56.637782Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Junchi Yan, Qingyun Li, Xue Yang, Ying Zeng, Yushi Chen","submitted_at":"2023-03-09T02:20:56Z","abstract_excerpt":"Existing oriented object detection methods commonly use metric AP$_{50}$ to measure the performance of the model. We argue that AP$_{50}$ is inherently unsuitable for oriented object detection due to its large tolerance in angle deviation. Therefore, we advocate using high-precision metric, e.g. AP$_{75}$, to measure the performance of models. In this paper, we propose an Aspect Ratio Sensitive Oriented Object Detector with Transformer, termed ARS-DETR, which exhibits a competitive performance in high-precision oriented object detection. Specifically, a new angle classification method, calling"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.04989","kind":"arxiv","version":3},"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/2303.04989/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":"2303.04989","created_at":"2026-07-05T08:04:56.637838+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.04989v3","created_at":"2026-07-05T08:04:56.637838+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.04989","created_at":"2026-07-05T08:04:56.637838+00:00"},{"alias_kind":"pith_short_12","alias_value":"42BTH7YRSISQ","created_at":"2026-07-05T08:04:56.637838+00:00"},{"alias_kind":"pith_short_16","alias_value":"42BTH7YRSISQYSVT","created_at":"2026-07-05T08:04:56.637838+00:00"},{"alias_kind":"pith_short_8","alias_value":"42BTH7YR","created_at":"2026-07-05T08:04:56.637838+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.20281","citing_title":"Fourier Series Coder: A Novel Perspective on Angle Boundary Discontinuity Problem for Oriented Object Detection","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/42BTH7YRSISQYSVT2RP7D5WYZU","json":"https://pith.science/pith/42BTH7YRSISQYSVT2RP7D5WYZU.json","graph_json":"https://pith.science/api/pith-number/42BTH7YRSISQYSVT2RP7D5WYZU/graph.json","events_json":"https://pith.science/api/pith-number/42BTH7YRSISQYSVT2RP7D5WYZU/events.json","paper":"https://pith.science/paper/42BTH7YR"},"agent_actions":{"view_html":"https://pith.science/pith/42BTH7YRSISQYSVT2RP7D5WYZU","download_json":"https://pith.science/pith/42BTH7YRSISQYSVT2RP7D5WYZU.json","view_paper":"https://pith.science/paper/42BTH7YR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.04989&json=true","fetch_graph":"https://pith.science/api/pith-number/42BTH7YRSISQYSVT2RP7D5WYZU/graph.json","fetch_events":"https://pith.science/api/pith-number/42BTH7YRSISQYSVT2RP7D5WYZU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/42BTH7YRSISQYSVT2RP7D5WYZU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/42BTH7YRSISQYSVT2RP7D5WYZU/action/storage_attestation","attest_author":"https://pith.science/pith/42BTH7YRSISQYSVT2RP7D5WYZU/action/author_attestation","sign_citation":"https://pith.science/pith/42BTH7YRSISQYSVT2RP7D5WYZU/action/citation_signature","submit_replication":"https://pith.science/pith/42BTH7YRSISQYSVT2RP7D5WYZU/action/replication_record"}},"created_at":"2026-07-05T08:04:56.637838+00:00","updated_at":"2026-07-05T08:04:56.637838+00:00"}