{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:TXOKJZBVY26TVXVTKPH4EZPU4B","short_pith_number":"pith:TXOKJZBV","schema_version":"1.0","canonical_sha256":"9ddca4e435c6bd3adeb353cfc265f4e06b92d2fef908cccdcdb3bde051beb712","source":{"kind":"arxiv","id":"2205.14659","version":1},"attestation_state":"computed","paper":{"title":"Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Liangyu Chai, Shengfeng He, Sucheng Ren, Wenxi Liu, Yongtuo Liu, Zheng Xiong","submitted_at":"2022-05-29T13:39:34Z","abstract_excerpt":"Crowd image is arguably one of the most laborious data to annotate. In this paper, we devote to reduce the massive demand of densely labeled crowd data, and propose a novel weakly-supervised setting, in which we leverage the binary ranking of two images with high-contrast crowd counts as training guidance. To enable training under this new setting, we convert the crowd count regression problem to a ranking potential prediction problem. In particular, we tailor a Siamese Ranking Network that predicts the potential scores of two images indicating the ordering of the counts. Hence, the ultimate g"},"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":"2205.14659","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-29T13:39:34Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"77f5bf4bdc835708df772301eed3af15c4b5392cd39f868917b16abe82f9f1f2","abstract_canon_sha256":"7fe8251951b31fcc23bddfdfbd5bc96e574c199257aeffb73f50d74dd5febd88"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:27:19.743945Z","signature_b64":"lPWac1l4NTBTjKZ/1pMyFLBuOCgb2bcZWqPud4T5Dms4M4tWRl3EAl3LgAC8LEg6HTRYXewGHvf8VxNemEXNBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ddca4e435c6bd3adeb353cfc265f4e06b92d2fef908cccdcdb3bde051beb712","last_reissued_at":"2026-07-05T04:27:19.743531Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:27:19.743531Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Liangyu Chai, Shengfeng He, Sucheng Ren, Wenxi Liu, Yongtuo Liu, Zheng Xiong","submitted_at":"2022-05-29T13:39:34Z","abstract_excerpt":"Crowd image is arguably one of the most laborious data to annotate. In this paper, we devote to reduce the massive demand of densely labeled crowd data, and propose a novel weakly-supervised setting, in which we leverage the binary ranking of two images with high-contrast crowd counts as training guidance. To enable training under this new setting, we convert the crowd count regression problem to a ranking potential prediction problem. In particular, we tailor a Siamese Ranking Network that predicts the potential scores of two images indicating the ordering of the counts. Hence, the ultimate g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.14659","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/2205.14659/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":"2205.14659","created_at":"2026-07-05T04:27:19.743589+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.14659v1","created_at":"2026-07-05T04:27:19.743589+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.14659","created_at":"2026-07-05T04:27:19.743589+00:00"},{"alias_kind":"pith_short_12","alias_value":"TXOKJZBVY26T","created_at":"2026-07-05T04:27:19.743589+00:00"},{"alias_kind":"pith_short_16","alias_value":"TXOKJZBVY26TVXVT","created_at":"2026-07-05T04:27:19.743589+00:00"},{"alias_kind":"pith_short_8","alias_value":"TXOKJZBV","created_at":"2026-07-05T04:27:19.743589+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/TXOKJZBVY26TVXVTKPH4EZPU4B","json":"https://pith.science/pith/TXOKJZBVY26TVXVTKPH4EZPU4B.json","graph_json":"https://pith.science/api/pith-number/TXOKJZBVY26TVXVTKPH4EZPU4B/graph.json","events_json":"https://pith.science/api/pith-number/TXOKJZBVY26TVXVTKPH4EZPU4B/events.json","paper":"https://pith.science/paper/TXOKJZBV"},"agent_actions":{"view_html":"https://pith.science/pith/TXOKJZBVY26TVXVTKPH4EZPU4B","download_json":"https://pith.science/pith/TXOKJZBVY26TVXVTKPH4EZPU4B.json","view_paper":"https://pith.science/paper/TXOKJZBV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.14659&json=true","fetch_graph":"https://pith.science/api/pith-number/TXOKJZBVY26TVXVTKPH4EZPU4B/graph.json","fetch_events":"https://pith.science/api/pith-number/TXOKJZBVY26TVXVTKPH4EZPU4B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TXOKJZBVY26TVXVTKPH4EZPU4B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TXOKJZBVY26TVXVTKPH4EZPU4B/action/storage_attestation","attest_author":"https://pith.science/pith/TXOKJZBVY26TVXVTKPH4EZPU4B/action/author_attestation","sign_citation":"https://pith.science/pith/TXOKJZBVY26TVXVTKPH4EZPU4B/action/citation_signature","submit_replication":"https://pith.science/pith/TXOKJZBVY26TVXVTKPH4EZPU4B/action/replication_record"}},"created_at":"2026-07-05T04:27:19.743589+00:00","updated_at":"2026-07-05T04:27:19.743589+00:00"}