{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LFY7A7EIRYTYY6JN6KMV76M3KQ","short_pith_number":"pith:LFY7A7EI","schema_version":"1.0","canonical_sha256":"5971f07c888e278c792df2995ff99b5404caa52647a072f2940010210b71d154","source":{"kind":"arxiv","id":"2405.11765","version":1},"attestation_state":"computed","paper":{"title":"DATR: Unsupervised Domain Adaptive Detection Transformer with Dataset-Level Adaptation and Prototypical Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianhong Han, Liang Chen, Yupei Wang","submitted_at":"2024-05-20T03:48:45Z","abstract_excerpt":"Object detectors frequently encounter significant performance degradation when confronted with domain gaps between collected data (source domain) and data from real-world applications (target domain). To address this task, numerous unsupervised domain adaptive detectors have been proposed, leveraging carefully designed feature alignment techniques. However, these techniques primarily align instance-level features in a class-agnostic manner, overlooking the differences between extracted features from different categories, which results in only limited improvement. Furthermore, the scope of curr"},"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":"2405.11765","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-20T03:48:45Z","cross_cats_sorted":[],"title_canon_sha256":"d009234c93ab30987dfda7ce5d86cfd1522d24222108fa2dba01114e8310cef1","abstract_canon_sha256":"a867c330624d650e09de2f1d546825fa0c5c6de6baab62bd19547f51fbc88f2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:20:55.392466Z","signature_b64":"ujQARro1pSiU0o9WjIKdbiiV6unRrN0Qo5SdGXIJ514bqkw5XfEbVab6a7OrOUSXvOG8pWUGGQeRe2fJioP0DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5971f07c888e278c792df2995ff99b5404caa52647a072f2940010210b71d154","last_reissued_at":"2026-07-05T08:20:55.391937Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:20:55.391937Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DATR: Unsupervised Domain Adaptive Detection Transformer with Dataset-Level Adaptation and Prototypical Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianhong Han, Liang Chen, Yupei Wang","submitted_at":"2024-05-20T03:48:45Z","abstract_excerpt":"Object detectors frequently encounter significant performance degradation when confronted with domain gaps between collected data (source domain) and data from real-world applications (target domain). To address this task, numerous unsupervised domain adaptive detectors have been proposed, leveraging carefully designed feature alignment techniques. However, these techniques primarily align instance-level features in a class-agnostic manner, overlooking the differences between extracted features from different categories, which results in only limited improvement. Furthermore, the scope of curr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.11765","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/2405.11765/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":"2405.11765","created_at":"2026-07-05T08:20:55.392000+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.11765v1","created_at":"2026-07-05T08:20:55.392000+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.11765","created_at":"2026-07-05T08:20:55.392000+00:00"},{"alias_kind":"pith_short_12","alias_value":"LFY7A7EIRYTY","created_at":"2026-07-05T08:20:55.392000+00:00"},{"alias_kind":"pith_short_16","alias_value":"LFY7A7EIRYTYY6JN","created_at":"2026-07-05T08:20:55.392000+00:00"},{"alias_kind":"pith_short_8","alias_value":"LFY7A7EI","created_at":"2026-07-05T08:20:55.392000+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/LFY7A7EIRYTYY6JN6KMV76M3KQ","json":"https://pith.science/pith/LFY7A7EIRYTYY6JN6KMV76M3KQ.json","graph_json":"https://pith.science/api/pith-number/LFY7A7EIRYTYY6JN6KMV76M3KQ/graph.json","events_json":"https://pith.science/api/pith-number/LFY7A7EIRYTYY6JN6KMV76M3KQ/events.json","paper":"https://pith.science/paper/LFY7A7EI"},"agent_actions":{"view_html":"https://pith.science/pith/LFY7A7EIRYTYY6JN6KMV76M3KQ","download_json":"https://pith.science/pith/LFY7A7EIRYTYY6JN6KMV76M3KQ.json","view_paper":"https://pith.science/paper/LFY7A7EI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.11765&json=true","fetch_graph":"https://pith.science/api/pith-number/LFY7A7EIRYTYY6JN6KMV76M3KQ/graph.json","fetch_events":"https://pith.science/api/pith-number/LFY7A7EIRYTYY6JN6KMV76M3KQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LFY7A7EIRYTYY6JN6KMV76M3KQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LFY7A7EIRYTYY6JN6KMV76M3KQ/action/storage_attestation","attest_author":"https://pith.science/pith/LFY7A7EIRYTYY6JN6KMV76M3KQ/action/author_attestation","sign_citation":"https://pith.science/pith/LFY7A7EIRYTYY6JN6KMV76M3KQ/action/citation_signature","submit_replication":"https://pith.science/pith/LFY7A7EIRYTYY6JN6KMV76M3KQ/action/replication_record"}},"created_at":"2026-07-05T08:20:55.392000+00:00","updated_at":"2026-07-05T08:20:55.392000+00:00"}