{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5NESYDZUBJ2TTXAVDRHCCD675N","short_pith_number":"pith:5NESYDZU","schema_version":"1.0","canonical_sha256":"eb492c0f340a7539dc151c4e210fdfeb662e8a443ace747701b6dd9cae6269f0","source":{"kind":"arxiv","id":"2307.00313","version":1},"attestation_state":"computed","paper":{"title":"PM-DETR: Domain Adaptive Prompt Memory for Object Detection with Transformers","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiaming Liu, Jiarui Wu, Peidong Jia, Senqiao Yang, Shanghang Zhang, Xiaodong Xie","submitted_at":"2023-07-01T12:02:24Z","abstract_excerpt":"The Transformer-based detectors (i.e., DETR) have demonstrated impressive performance on end-to-end object detection. However, transferring DETR to different data distributions may lead to a significant performance degradation. Existing adaptation techniques focus on model-based approaches, which aim to leverage feature alignment to narrow the distribution shift between different domains. In this study, we propose a hierarchical Prompt Domain Memory (PDM) for adapting detection transformers to different distributions. PDM comprehensively leverages the prompt memory to extract domain-specific k"},"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":"2307.00313","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-01T12:02:24Z","cross_cats_sorted":[],"title_canon_sha256":"6323d2a167b3f9e58e45391c9ce074e3740fe9bf826a4b30cb4244ed65c5dcd6","abstract_canon_sha256":"3a896d38d4166a94502fa935d1df1ad4354f6356d4ba5f97f69c765038c2224f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:26:54.751356Z","signature_b64":"njVOtK3jBxPySlCxPZlN5AvV5lbLEZn8itk3vDaC1YN6HfsQhsquh0WvLDsHGJVmwonYSCDJO0uXUOXVVsovBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb492c0f340a7539dc151c4e210fdfeb662e8a443ace747701b6dd9cae6269f0","last_reissued_at":"2026-07-05T06:26:54.750961Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:26:54.750961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PM-DETR: Domain Adaptive Prompt Memory for Object Detection with Transformers","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiaming Liu, Jiarui Wu, Peidong Jia, Senqiao Yang, Shanghang Zhang, Xiaodong Xie","submitted_at":"2023-07-01T12:02:24Z","abstract_excerpt":"The Transformer-based detectors (i.e., DETR) have demonstrated impressive performance on end-to-end object detection. However, transferring DETR to different data distributions may lead to a significant performance degradation. Existing adaptation techniques focus on model-based approaches, which aim to leverage feature alignment to narrow the distribution shift between different domains. In this study, we propose a hierarchical Prompt Domain Memory (PDM) for adapting detection transformers to different distributions. PDM comprehensively leverages the prompt memory to extract domain-specific k"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.00313","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/2307.00313/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":"2307.00313","created_at":"2026-07-05T06:26:54.751018+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.00313v1","created_at":"2026-07-05T06:26:54.751018+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.00313","created_at":"2026-07-05T06:26:54.751018+00:00"},{"alias_kind":"pith_short_12","alias_value":"5NESYDZUBJ2T","created_at":"2026-07-05T06:26:54.751018+00:00"},{"alias_kind":"pith_short_16","alias_value":"5NESYDZUBJ2TTXAV","created_at":"2026-07-05T06:26:54.751018+00:00"},{"alias_kind":"pith_short_8","alias_value":"5NESYDZU","created_at":"2026-07-05T06:26:54.751018+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/5NESYDZUBJ2TTXAVDRHCCD675N","json":"https://pith.science/pith/5NESYDZUBJ2TTXAVDRHCCD675N.json","graph_json":"https://pith.science/api/pith-number/5NESYDZUBJ2TTXAVDRHCCD675N/graph.json","events_json":"https://pith.science/api/pith-number/5NESYDZUBJ2TTXAVDRHCCD675N/events.json","paper":"https://pith.science/paper/5NESYDZU"},"agent_actions":{"view_html":"https://pith.science/pith/5NESYDZUBJ2TTXAVDRHCCD675N","download_json":"https://pith.science/pith/5NESYDZUBJ2TTXAVDRHCCD675N.json","view_paper":"https://pith.science/paper/5NESYDZU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.00313&json=true","fetch_graph":"https://pith.science/api/pith-number/5NESYDZUBJ2TTXAVDRHCCD675N/graph.json","fetch_events":"https://pith.science/api/pith-number/5NESYDZUBJ2TTXAVDRHCCD675N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5NESYDZUBJ2TTXAVDRHCCD675N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5NESYDZUBJ2TTXAVDRHCCD675N/action/storage_attestation","attest_author":"https://pith.science/pith/5NESYDZUBJ2TTXAVDRHCCD675N/action/author_attestation","sign_citation":"https://pith.science/pith/5NESYDZUBJ2TTXAVDRHCCD675N/action/citation_signature","submit_replication":"https://pith.science/pith/5NESYDZUBJ2TTXAVDRHCCD675N/action/replication_record"}},"created_at":"2026-07-05T06:26:54.751018+00:00","updated_at":"2026-07-05T06:26:54.751018+00:00"}