{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:VFILOIZ2QO2ECOEX7VCMRTT7RM","short_pith_number":"pith:VFILOIZ2","schema_version":"1.0","canonical_sha256":"a950b7233a83b4413897fd44c8ce7f8b3a01789270eedc804a87a735a7a4282a","source":{"kind":"arxiv","id":"2207.10988","version":2},"attestation_state":"computed","paper":{"title":"Few-shot Object Counting and Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chau Pham, Khoi Nguyen, Minh Hoai, Thanh Nguyen","submitted_at":"2022-07-22T10:09:18Z","abstract_excerpt":"We tackle a new task of few-shot object counting and detection. Given a few exemplar bounding boxes of a target object class, we seek to count and detect all objects of the target class. This task shares the same supervision as the few-shot object counting but additionally outputs the object bounding boxes along with the total object count. To address this challenging problem, we introduce a novel two-stage training strategy and a novel uncertainty-aware few-shot object detector: Counting-DETR. The former is aimed at generating pseudo ground-truth bounding boxes to train the latter. The latter"},"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":"2207.10988","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-22T10:09:18Z","cross_cats_sorted":[],"title_canon_sha256":"b09a34dd14cbcecc1dbc94c8ae7f50f065c839f3fce8938f91766120b5695153","abstract_canon_sha256":"6ea740677549d1d959622e568a9722caeedbad888a55b6da16346ff0e74371bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:44:21.853284Z","signature_b64":"bb4dLbUinFbqeHbMG60WqWQH3iWNk/ACRYqro6A6BH/opkliTnlOfq4huAhqYBBWv5eZ89Vw+C7NI02CqBZABQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a950b7233a83b4413897fd44c8ce7f8b3a01789270eedc804a87a735a7a4282a","last_reissued_at":"2026-07-05T04:44:21.852799Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:44:21.852799Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Few-shot Object Counting and Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chau Pham, Khoi Nguyen, Minh Hoai, Thanh Nguyen","submitted_at":"2022-07-22T10:09:18Z","abstract_excerpt":"We tackle a new task of few-shot object counting and detection. Given a few exemplar bounding boxes of a target object class, we seek to count and detect all objects of the target class. This task shares the same supervision as the few-shot object counting but additionally outputs the object bounding boxes along with the total object count. To address this challenging problem, we introduce a novel two-stage training strategy and a novel uncertainty-aware few-shot object detector: Counting-DETR. The former is aimed at generating pseudo ground-truth bounding boxes to train the latter. The latter"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.10988","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/2207.10988/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":"2207.10988","created_at":"2026-07-05T04:44:21.852858+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.10988v2","created_at":"2026-07-05T04:44:21.852858+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.10988","created_at":"2026-07-05T04:44:21.852858+00:00"},{"alias_kind":"pith_short_12","alias_value":"VFILOIZ2QO2E","created_at":"2026-07-05T04:44:21.852858+00:00"},{"alias_kind":"pith_short_16","alias_value":"VFILOIZ2QO2ECOEX","created_at":"2026-07-05T04:44:21.852858+00:00"},{"alias_kind":"pith_short_8","alias_value":"VFILOIZ2","created_at":"2026-07-05T04:44:21.852858+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.16778","citing_title":"Single Domain Generalization for Few-Shot Counting via Universal Representation Matching","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VFILOIZ2QO2ECOEX7VCMRTT7RM","json":"https://pith.science/pith/VFILOIZ2QO2ECOEX7VCMRTT7RM.json","graph_json":"https://pith.science/api/pith-number/VFILOIZ2QO2ECOEX7VCMRTT7RM/graph.json","events_json":"https://pith.science/api/pith-number/VFILOIZ2QO2ECOEX7VCMRTT7RM/events.json","paper":"https://pith.science/paper/VFILOIZ2"},"agent_actions":{"view_html":"https://pith.science/pith/VFILOIZ2QO2ECOEX7VCMRTT7RM","download_json":"https://pith.science/pith/VFILOIZ2QO2ECOEX7VCMRTT7RM.json","view_paper":"https://pith.science/paper/VFILOIZ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.10988&json=true","fetch_graph":"https://pith.science/api/pith-number/VFILOIZ2QO2ECOEX7VCMRTT7RM/graph.json","fetch_events":"https://pith.science/api/pith-number/VFILOIZ2QO2ECOEX7VCMRTT7RM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VFILOIZ2QO2ECOEX7VCMRTT7RM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VFILOIZ2QO2ECOEX7VCMRTT7RM/action/storage_attestation","attest_author":"https://pith.science/pith/VFILOIZ2QO2ECOEX7VCMRTT7RM/action/author_attestation","sign_citation":"https://pith.science/pith/VFILOIZ2QO2ECOEX7VCMRTT7RM/action/citation_signature","submit_replication":"https://pith.science/pith/VFILOIZ2QO2ECOEX7VCMRTT7RM/action/replication_record"}},"created_at":"2026-07-05T04:44:21.852858+00:00","updated_at":"2026-07-05T04:44:21.852858+00:00"}