{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:VBL2ZAFGAEKLTDJAAZRGBTA73G","short_pith_number":"pith:VBL2ZAFG","schema_version":"1.0","canonical_sha256":"a857ac80a60114b98d20066260cc1fd9be26b800dd07ca7e67fc13ab392a6066","source":{"kind":"arxiv","id":"2212.09589","version":1},"attestation_state":"computed","paper":{"title":"Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Erickson R. Nascimento, Felipe Cadar, Guilherme Potje, Renato Martins, Welerson Melo","submitted_at":"2022-12-13T11:59:09Z","abstract_excerpt":"We present a novel learned keypoint detection method designed to maximize the number of correct matches for the task of non-rigid image correspondence. Our training framework uses true correspondences, obtained by matching annotated image pairs with a predefined descriptor extractor, as a ground-truth to train a convolutional neural network (CNN). We optimize the model architecture by applying known geometric transformations to images as the supervisory signal. Experiments show that our method outperforms the state-of-the-art keypoint detector on real images of non-rigid objects by 20 p.p. on "},"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":"2212.09589","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-13T11:59:09Z","cross_cats_sorted":[],"title_canon_sha256":"70869b61ebead096cad437dbae181c55f44d819b8a4c446d9099faa99c9b3e0e","abstract_canon_sha256":"879061d236443f3d97633886d02737bbaf9d115f3e2de3f3a94112dfd26e91d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:26:23.780830Z","signature_b64":"oeY1yapidhAMIyHAvBGAr8aOwu3LFUNd5Avy0HLAnQyWpR38X+CPi3GWhn1c8dz0NJEHEXxyL0RhUd0nqZwpBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a857ac80a60114b98d20066260cc1fd9be26b800dd07ca7e67fc13ab392a6066","last_reissued_at":"2026-07-05T05:26:23.780345Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:26:23.780345Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Erickson R. Nascimento, Felipe Cadar, Guilherme Potje, Renato Martins, Welerson Melo","submitted_at":"2022-12-13T11:59:09Z","abstract_excerpt":"We present a novel learned keypoint detection method designed to maximize the number of correct matches for the task of non-rigid image correspondence. Our training framework uses true correspondences, obtained by matching annotated image pairs with a predefined descriptor extractor, as a ground-truth to train a convolutional neural network (CNN). We optimize the model architecture by applying known geometric transformations to images as the supervisory signal. Experiments show that our method outperforms the state-of-the-art keypoint detector on real images of non-rigid objects by 20 p.p. on "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09589","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/2212.09589/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":"2212.09589","created_at":"2026-07-05T05:26:23.780404+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.09589v1","created_at":"2026-07-05T05:26:23.780404+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09589","created_at":"2026-07-05T05:26:23.780404+00:00"},{"alias_kind":"pith_short_12","alias_value":"VBL2ZAFGAEKL","created_at":"2026-07-05T05:26:23.780404+00:00"},{"alias_kind":"pith_short_16","alias_value":"VBL2ZAFGAEKLTDJA","created_at":"2026-07-05T05:26:23.780404+00:00"},{"alias_kind":"pith_short_8","alias_value":"VBL2ZAFG","created_at":"2026-07-05T05:26:23.780404+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/VBL2ZAFGAEKLTDJAAZRGBTA73G","json":"https://pith.science/pith/VBL2ZAFGAEKLTDJAAZRGBTA73G.json","graph_json":"https://pith.science/api/pith-number/VBL2ZAFGAEKLTDJAAZRGBTA73G/graph.json","events_json":"https://pith.science/api/pith-number/VBL2ZAFGAEKLTDJAAZRGBTA73G/events.json","paper":"https://pith.science/paper/VBL2ZAFG"},"agent_actions":{"view_html":"https://pith.science/pith/VBL2ZAFGAEKLTDJAAZRGBTA73G","download_json":"https://pith.science/pith/VBL2ZAFGAEKLTDJAAZRGBTA73G.json","view_paper":"https://pith.science/paper/VBL2ZAFG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.09589&json=true","fetch_graph":"https://pith.science/api/pith-number/VBL2ZAFGAEKLTDJAAZRGBTA73G/graph.json","fetch_events":"https://pith.science/api/pith-number/VBL2ZAFGAEKLTDJAAZRGBTA73G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VBL2ZAFGAEKLTDJAAZRGBTA73G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VBL2ZAFGAEKLTDJAAZRGBTA73G/action/storage_attestation","attest_author":"https://pith.science/pith/VBL2ZAFGAEKLTDJAAZRGBTA73G/action/author_attestation","sign_citation":"https://pith.science/pith/VBL2ZAFGAEKLTDJAAZRGBTA73G/action/citation_signature","submit_replication":"https://pith.science/pith/VBL2ZAFGAEKLTDJAAZRGBTA73G/action/replication_record"}},"created_at":"2026-07-05T05:26:23.780404+00:00","updated_at":"2026-07-05T05:26:23.780404+00:00"}