{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:FASF4FAX4PHMYGBEWCKFXOMXWA","short_pith_number":"pith:FASF4FAX","schema_version":"1.0","canonical_sha256":"28245e1417e3cecc1824b0945bb997b0383991e699814811b5bae26e8f796d06","source":{"kind":"arxiv","id":"2201.02861","version":2},"attestation_state":"computed","paper":{"title":"Decoupling Makes Weakly Supervised Local Feature Better","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kai Xu, Kunhong Li, Li Liu, Longguang Wang, Qing Ran, Yulan Guo","submitted_at":"2022-01-08T16:51:02Z","abstract_excerpt":"Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences. However, since weak supervision cannot distinguish the losses caused by the detection and description steps, directly conducting weakly supervised learning within a joint describe-then-detect pipeline suffers limited performance. In this paper, we propose a decoupled describe-then-detect pipeline tailored for weakly supervised local feature learning. Within our pipeline, the detection step is decoupled from the description step and postpone"},"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":"2201.02861","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-01-08T16:51:02Z","cross_cats_sorted":[],"title_canon_sha256":"e3ed3ed469c3fcffee749634b81ef5aaff8ec59cffbda1eb4d317543139aa6f3","abstract_canon_sha256":"d641b3d2ec8cdd21cb8cd33707d835ae09cc7f7b8af9f93798cc6788d2ffc6e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:08:41.898160Z","signature_b64":"UzcHyVDsyeZFirm7ixrNmu3MEuzNh9YoLvmpXfgAfDFSNKrvFSstyVyIyMqHpHBED8fm8d43W5APwkFR9i97AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"28245e1417e3cecc1824b0945bb997b0383991e699814811b5bae26e8f796d06","last_reissued_at":"2026-07-05T04:08:41.897601Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:08:41.897601Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Decoupling Makes Weakly Supervised Local Feature Better","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kai Xu, Kunhong Li, Li Liu, Longguang Wang, Qing Ran, Yulan Guo","submitted_at":"2022-01-08T16:51:02Z","abstract_excerpt":"Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences. However, since weak supervision cannot distinguish the losses caused by the detection and description steps, directly conducting weakly supervised learning within a joint describe-then-detect pipeline suffers limited performance. In this paper, we propose a decoupled describe-then-detect pipeline tailored for weakly supervised local feature learning. Within our pipeline, the detection step is decoupled from the description step and postpone"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.02861","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/2201.02861/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":"2201.02861","created_at":"2026-07-05T04:08:41.897657+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.02861v2","created_at":"2026-07-05T04:08:41.897657+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.02861","created_at":"2026-07-05T04:08:41.897657+00:00"},{"alias_kind":"pith_short_12","alias_value":"FASF4FAX4PHM","created_at":"2026-07-05T04:08:41.897657+00:00"},{"alias_kind":"pith_short_16","alias_value":"FASF4FAX4PHMYGBE","created_at":"2026-07-05T04:08:41.897657+00:00"},{"alias_kind":"pith_short_8","alias_value":"FASF4FAX","created_at":"2026-07-05T04:08:41.897657+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/FASF4FAX4PHMYGBEWCKFXOMXWA","json":"https://pith.science/pith/FASF4FAX4PHMYGBEWCKFXOMXWA.json","graph_json":"https://pith.science/api/pith-number/FASF4FAX4PHMYGBEWCKFXOMXWA/graph.json","events_json":"https://pith.science/api/pith-number/FASF4FAX4PHMYGBEWCKFXOMXWA/events.json","paper":"https://pith.science/paper/FASF4FAX"},"agent_actions":{"view_html":"https://pith.science/pith/FASF4FAX4PHMYGBEWCKFXOMXWA","download_json":"https://pith.science/pith/FASF4FAX4PHMYGBEWCKFXOMXWA.json","view_paper":"https://pith.science/paper/FASF4FAX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.02861&json=true","fetch_graph":"https://pith.science/api/pith-number/FASF4FAX4PHMYGBEWCKFXOMXWA/graph.json","fetch_events":"https://pith.science/api/pith-number/FASF4FAX4PHMYGBEWCKFXOMXWA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FASF4FAX4PHMYGBEWCKFXOMXWA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FASF4FAX4PHMYGBEWCKFXOMXWA/action/storage_attestation","attest_author":"https://pith.science/pith/FASF4FAX4PHMYGBEWCKFXOMXWA/action/author_attestation","sign_citation":"https://pith.science/pith/FASF4FAX4PHMYGBEWCKFXOMXWA/action/citation_signature","submit_replication":"https://pith.science/pith/FASF4FAX4PHMYGBEWCKFXOMXWA/action/replication_record"}},"created_at":"2026-07-05T04:08:41.897657+00:00","updated_at":"2026-07-05T04:08:41.897657+00:00"}