{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7LSSZWQ2XVYOWA4ZIHURJPACUF","short_pith_number":"pith:7LSSZWQ2","schema_version":"1.0","canonical_sha256":"fae52cda1abd70eb039941e914bc02a178dcbd610ea90bbcfa85534ffd786477","source":{"kind":"arxiv","id":"2301.09249","version":2},"attestation_state":"computed","paper":{"title":"Exploring Active 3D Object Detection from a Generalization Perspective","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Mahsa Baktashmotlagh, Xin Yu, Yadan Luo, Zhuoxiao Chen, Zi Huang, Zijian Wang","submitted_at":"2023-01-23T02:43:03Z","abstract_excerpt":"To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is a promising solution that learns to select only a small portion of unlabeled data to annotate, without compromising model performance. Our empirical study, however, suggests that mainstream uncertainty-based and diversity-based active learning policies are not effective when applied in the 3D detection task, as they fail to balance the trade-off between point cloud informativeness and box-level annotation costs. To overcome this limitation, we jointly investigate three novel criteria in our framework C"},"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":"2301.09249","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-01-23T02:43:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"65103e83ffdf9aa74597942b06289c060a4e21e29fee896a18f4c4664b74cce8","abstract_canon_sha256":"570c88803d31b20a33d5100e62d83871476a2407f549c3a647f9f9011e05c7af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:39:55.458181Z","signature_b64":"iG3vxjrfTqz1SS4/AjMdNtmNj/KjOse27KwWQ/aB4hcR2G/gDSYGD+Bkc/G9D8vIQKRFjISH4vKJhqF421EJAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fae52cda1abd70eb039941e914bc02a178dcbd610ea90bbcfa85534ffd786477","last_reissued_at":"2026-07-05T05:39:55.457784Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:39:55.457784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring Active 3D Object Detection from a Generalization Perspective","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Mahsa Baktashmotlagh, Xin Yu, Yadan Luo, Zhuoxiao Chen, Zi Huang, Zijian Wang","submitted_at":"2023-01-23T02:43:03Z","abstract_excerpt":"To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is a promising solution that learns to select only a small portion of unlabeled data to annotate, without compromising model performance. Our empirical study, however, suggests that mainstream uncertainty-based and diversity-based active learning policies are not effective when applied in the 3D detection task, as they fail to balance the trade-off between point cloud informativeness and box-level annotation costs. To overcome this limitation, we jointly investigate three novel criteria in our framework C"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.09249","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/2301.09249/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":"2301.09249","created_at":"2026-07-05T05:39:55.457841+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.09249v2","created_at":"2026-07-05T05:39:55.457841+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.09249","created_at":"2026-07-05T05:39:55.457841+00:00"},{"alias_kind":"pith_short_12","alias_value":"7LSSZWQ2XVYO","created_at":"2026-07-05T05:39:55.457841+00:00"},{"alias_kind":"pith_short_16","alias_value":"7LSSZWQ2XVYOWA4Z","created_at":"2026-07-05T05:39:55.457841+00:00"},{"alias_kind":"pith_short_8","alias_value":"7LSSZWQ2","created_at":"2026-07-05T05:39:55.457841+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.10935","citing_title":"HQ-OV3D: A High Box Quality Open-World 3D Detection Framework based on Diffision Model","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7LSSZWQ2XVYOWA4ZIHURJPACUF","json":"https://pith.science/pith/7LSSZWQ2XVYOWA4ZIHURJPACUF.json","graph_json":"https://pith.science/api/pith-number/7LSSZWQ2XVYOWA4ZIHURJPACUF/graph.json","events_json":"https://pith.science/api/pith-number/7LSSZWQ2XVYOWA4ZIHURJPACUF/events.json","paper":"https://pith.science/paper/7LSSZWQ2"},"agent_actions":{"view_html":"https://pith.science/pith/7LSSZWQ2XVYOWA4ZIHURJPACUF","download_json":"https://pith.science/pith/7LSSZWQ2XVYOWA4ZIHURJPACUF.json","view_paper":"https://pith.science/paper/7LSSZWQ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.09249&json=true","fetch_graph":"https://pith.science/api/pith-number/7LSSZWQ2XVYOWA4ZIHURJPACUF/graph.json","fetch_events":"https://pith.science/api/pith-number/7LSSZWQ2XVYOWA4ZIHURJPACUF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7LSSZWQ2XVYOWA4ZIHURJPACUF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7LSSZWQ2XVYOWA4ZIHURJPACUF/action/storage_attestation","attest_author":"https://pith.science/pith/7LSSZWQ2XVYOWA4ZIHURJPACUF/action/author_attestation","sign_citation":"https://pith.science/pith/7LSSZWQ2XVYOWA4ZIHURJPACUF/action/citation_signature","submit_replication":"https://pith.science/pith/7LSSZWQ2XVYOWA4ZIHURJPACUF/action/replication_record"}},"created_at":"2026-07-05T05:39:55.457841+00:00","updated_at":"2026-07-05T05:39:55.457841+00:00"}