{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:JVEXD7PQEUFWBBKOBZ3H3JBI5J","short_pith_number":"pith:JVEXD7PQ","schema_version":"1.0","canonical_sha256":"4d4971fdf0250b60854e0e767da428ea40ded9b993eaabdfad6c04581b03c195","source":{"kind":"arxiv","id":"2207.13339","version":3},"attestation_state":"computed","paper":{"title":"ALBench: A Framework for Evaluating Active Learning in Object Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Li-Jia Li, Manmohan Chandraker, Rinyoichi Takezoe, Shiliang Zhang, Vijay K. Narayanan, Wenze Hu, Xiaoyu Wang, Zhanpeng Feng","submitted_at":"2022-07-27T07:46:23Z","abstract_excerpt":"Active learning is an important technology for automated machine learning systems. In contrast to Neural Architecture Search (NAS) which aims at automating neural network architecture design, active learning aims at automating training data selection. It is especially critical for training a long-tailed task, in which positive samples are sparsely distributed. Active learning alleviates the expensive data annotation issue through incrementally training models powered with efficient data selection. Instead of annotating all unlabeled samples, it iteratively selects and annotates the most valuab"},"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":"2207.13339","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-07-27T07:46:23Z","cross_cats_sorted":[],"title_canon_sha256":"d82c42fb02f9d8584135b12cb3d4547ef6b7623f43724802430f6e17754ae3b5","abstract_canon_sha256":"be2a80a1dd1d6c8738b357bcf7b3e53108060d458a644ec65ea5bde5e4a1f69f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:19:17.957211Z","signature_b64":"cCHm+VZqILit3ThOQRz/DPyPm04jkTUwj2pbG04al5v/cVPLqgDjlCS7NCL+SV+04x+yyd2cQvFPFlqUw/wABQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d4971fdf0250b60854e0e767da428ea40ded9b993eaabdfad6c04581b03c195","last_reissued_at":"2026-07-05T05:19:17.956687Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:19:17.956687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ALBench: A Framework for Evaluating Active Learning in Object Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Li-Jia Li, Manmohan Chandraker, Rinyoichi Takezoe, Shiliang Zhang, Vijay K. Narayanan, Wenze Hu, Xiaoyu Wang, Zhanpeng Feng","submitted_at":"2022-07-27T07:46:23Z","abstract_excerpt":"Active learning is an important technology for automated machine learning systems. In contrast to Neural Architecture Search (NAS) which aims at automating neural network architecture design, active learning aims at automating training data selection. It is especially critical for training a long-tailed task, in which positive samples are sparsely distributed. Active learning alleviates the expensive data annotation issue through incrementally training models powered with efficient data selection. Instead of annotating all unlabeled samples, it iteratively selects and annotates the most valuab"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.13339","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/2207.13339/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":"2207.13339","created_at":"2026-07-05T05:19:17.956755+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.13339v3","created_at":"2026-07-05T05:19:17.956755+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.13339","created_at":"2026-07-05T05:19:17.956755+00:00"},{"alias_kind":"pith_short_12","alias_value":"JVEXD7PQEUFW","created_at":"2026-07-05T05:19:17.956755+00:00"},{"alias_kind":"pith_short_16","alias_value":"JVEXD7PQEUFWBBKO","created_at":"2026-07-05T05:19:17.956755+00:00"},{"alias_kind":"pith_short_8","alias_value":"JVEXD7PQ","created_at":"2026-07-05T05:19:17.956755+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.19906","citing_title":"Streamlining the Development of Active Learning Methods in Real-World Object Detection","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JVEXD7PQEUFWBBKOBZ3H3JBI5J","json":"https://pith.science/pith/JVEXD7PQEUFWBBKOBZ3H3JBI5J.json","graph_json":"https://pith.science/api/pith-number/JVEXD7PQEUFWBBKOBZ3H3JBI5J/graph.json","events_json":"https://pith.science/api/pith-number/JVEXD7PQEUFWBBKOBZ3H3JBI5J/events.json","paper":"https://pith.science/paper/JVEXD7PQ"},"agent_actions":{"view_html":"https://pith.science/pith/JVEXD7PQEUFWBBKOBZ3H3JBI5J","download_json":"https://pith.science/pith/JVEXD7PQEUFWBBKOBZ3H3JBI5J.json","view_paper":"https://pith.science/paper/JVEXD7PQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.13339&json=true","fetch_graph":"https://pith.science/api/pith-number/JVEXD7PQEUFWBBKOBZ3H3JBI5J/graph.json","fetch_events":"https://pith.science/api/pith-number/JVEXD7PQEUFWBBKOBZ3H3JBI5J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JVEXD7PQEUFWBBKOBZ3H3JBI5J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JVEXD7PQEUFWBBKOBZ3H3JBI5J/action/storage_attestation","attest_author":"https://pith.science/pith/JVEXD7PQEUFWBBKOBZ3H3JBI5J/action/author_attestation","sign_citation":"https://pith.science/pith/JVEXD7PQEUFWBBKOBZ3H3JBI5J/action/citation_signature","submit_replication":"https://pith.science/pith/JVEXD7PQEUFWBBKOBZ3H3JBI5J/action/replication_record"}},"created_at":"2026-07-05T05:19:17.956755+00:00","updated_at":"2026-07-05T05:19:17.956755+00:00"}