{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:HYJIGEICQNHGPBSUDD2GORCBRH","short_pith_number":"pith:HYJIGEIC","schema_version":"1.0","canonical_sha256":"3e12831102834e67865418f467444189d849e4d83c0ebb0da01594efdadab553","source":{"kind":"arxiv","id":"2107.10834","version":1},"attestation_state":"computed","paper":{"title":"Query2Label: A Simple Transformer Way to Multi-Label Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hang Su, Jun Zhu, Lei Zhang, Shilong Liu, Xiao Yang","submitted_at":"2021-07-22T17:49:25Z","abstract_excerpt":"This paper presents a simple and effective approach to solving the multi-label classification problem. The proposed approach leverages Transformer decoders to query the existence of a class label. The use of Transformer is rooted in the need of extracting local discriminative features adaptively for different labels, which is a strongly desired property due to the existence of multiple objects in one image. The built-in cross-attention module in the Transformer decoder offers an effective way to use label embeddings as queries to probe and pool class-related features from a feature map compute"},"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":"2107.10834","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-07-22T17:49:25Z","cross_cats_sorted":[],"title_canon_sha256":"f7628067c1909e82b8e28da279b9e643c11684ded3fe71357831f90761f2fbb8","abstract_canon_sha256":"032714267f73f10968986b74b6aaf2b690bb705bf58e08d6e6625429dc3d0ae2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:00:03.994382Z","signature_b64":"90BU3erzSujwPGcnVsnmra54QaGFOG+yxSiflpfmQxMkHFa4SkqUfq84cqSLIWlDy3wwLojQR9wQojp1+3i9Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e12831102834e67865418f467444189d849e4d83c0ebb0da01594efdadab553","last_reissued_at":"2026-07-05T03:00:03.994013Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:00:03.994013Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Query2Label: A Simple Transformer Way to Multi-Label Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hang Su, Jun Zhu, Lei Zhang, Shilong Liu, Xiao Yang","submitted_at":"2021-07-22T17:49:25Z","abstract_excerpt":"This paper presents a simple and effective approach to solving the multi-label classification problem. The proposed approach leverages Transformer decoders to query the existence of a class label. The use of Transformer is rooted in the need of extracting local discriminative features adaptively for different labels, which is a strongly desired property due to the existence of multiple objects in one image. The built-in cross-attention module in the Transformer decoder offers an effective way to use label embeddings as queries to probe and pool class-related features from a feature map compute"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.10834","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/2107.10834/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":"2107.10834","created_at":"2026-07-05T03:00:03.994073+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.10834v1","created_at":"2026-07-05T03:00:03.994073+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.10834","created_at":"2026-07-05T03:00:03.994073+00:00"},{"alias_kind":"pith_short_12","alias_value":"HYJIGEICQNHG","created_at":"2026-07-05T03:00:03.994073+00:00"},{"alias_kind":"pith_short_16","alias_value":"HYJIGEICQNHGPBSU","created_at":"2026-07-05T03:00:03.994073+00:00"},{"alias_kind":"pith_short_8","alias_value":"HYJIGEIC","created_at":"2026-07-05T03:00:03.994073+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22890","citing_title":"PHOEBI: An Open-World Benchmark for Bacterial Identification in Phase-Contrast Microscopy","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05455","citing_title":"Disentangled Fine-Grained Prototype Learning for Incomplete Image-Tabular Classification","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2606.07585","citing_title":"Multimodal Group Emotion Recognition In-the-Wild Towards a Privacy-Safe Non-Individual Approach","ref_index":143,"is_internal_anchor":false},{"citing_arxiv_id":"2305.07152","citing_title":"Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15397","citing_title":"ELDOR: A Dataset and Benchmark for Illegal Gold Mining in the Amazon Rainforest","ref_index":92,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08819","citing_title":"SenBen: Sensitive Scene Graphs for Explainable Content Moderation","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15555","citing_title":"CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HYJIGEICQNHGPBSUDD2GORCBRH","json":"https://pith.science/pith/HYJIGEICQNHGPBSUDD2GORCBRH.json","graph_json":"https://pith.science/api/pith-number/HYJIGEICQNHGPBSUDD2GORCBRH/graph.json","events_json":"https://pith.science/api/pith-number/HYJIGEICQNHGPBSUDD2GORCBRH/events.json","paper":"https://pith.science/paper/HYJIGEIC"},"agent_actions":{"view_html":"https://pith.science/pith/HYJIGEICQNHGPBSUDD2GORCBRH","download_json":"https://pith.science/pith/HYJIGEICQNHGPBSUDD2GORCBRH.json","view_paper":"https://pith.science/paper/HYJIGEIC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.10834&json=true","fetch_graph":"https://pith.science/api/pith-number/HYJIGEICQNHGPBSUDD2GORCBRH/graph.json","fetch_events":"https://pith.science/api/pith-number/HYJIGEICQNHGPBSUDD2GORCBRH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HYJIGEICQNHGPBSUDD2GORCBRH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HYJIGEICQNHGPBSUDD2GORCBRH/action/storage_attestation","attest_author":"https://pith.science/pith/HYJIGEICQNHGPBSUDD2GORCBRH/action/author_attestation","sign_citation":"https://pith.science/pith/HYJIGEICQNHGPBSUDD2GORCBRH/action/citation_signature","submit_replication":"https://pith.science/pith/HYJIGEICQNHGPBSUDD2GORCBRH/action/replication_record"}},"created_at":"2026-07-05T03:00:03.994073+00:00","updated_at":"2026-07-05T03:00:03.994073+00:00"}