{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:2TZZSNLDISQZSCZGF4ZV2YIC7P","short_pith_number":"pith:2TZZSNLD","schema_version":"1.0","canonical_sha256":"d4f399356344a1990b262f335d6102fbfa01524b20b9d0eb74faba02262bc90f","source":{"kind":"arxiv","id":"1904.00320","version":1},"attestation_state":"computed","paper":{"title":"NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Zhao, Chi Li, Jiaqi Yang, Xin Li, Zhiguo Cao","submitted_at":"2019-03-31T01:50:37Z","abstract_excerpt":"Feature correspondence selection is pivotal to many feature-matching based tasks in computer vision. Searching for spatially k-nearest neighbors is a common strategy for extracting local information in many previous works. However, there is no guarantee that the spatially k-nearest neighbors of correspondences are consistent because the spatial distribution of false correspondences is often irregular. To address this issue, we present a compatibility-specific mining method to search for consistent neighbors. Moreover, in order to extract and aggregate more reliable features from neighbors, we "},"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":"1904.00320","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-31T01:50:37Z","cross_cats_sorted":[],"title_canon_sha256":"bc26946a51c95b4cb7ba105bd852131da0ab4fc488d370941b104f38d6e5af7e","abstract_canon_sha256":"74188702edfc4a4f14620cb1bdfcaa55ddd0ed45377a3422bfd1e52f0b80a8a6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:48.781479Z","signature_b64":"sxFAgTxCztOVGnjQrjVztxRhWI1Es5Sc+GzF2/45oEvfOj7nwXUmKxaE78RvZIFMc4BolXHgRozVKHYN40AjAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d4f399356344a1990b262f335d6102fbfa01524b20b9d0eb74faba02262bc90f","last_reissued_at":"2026-05-17T23:49:48.780929Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:48.780929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Zhao, Chi Li, Jiaqi Yang, Xin Li, Zhiguo Cao","submitted_at":"2019-03-31T01:50:37Z","abstract_excerpt":"Feature correspondence selection is pivotal to many feature-matching based tasks in computer vision. Searching for spatially k-nearest neighbors is a common strategy for extracting local information in many previous works. However, there is no guarantee that the spatially k-nearest neighbors of correspondences are consistent because the spatial distribution of false correspondences is often irregular. To address this issue, we present a compatibility-specific mining method to search for consistent neighbors. Moreover, in order to extract and aggregate more reliable features from neighbors, we "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.00320","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":""},"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":"1904.00320","created_at":"2026-05-17T23:49:48.781012+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.00320v1","created_at":"2026-05-17T23:49:48.781012+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.00320","created_at":"2026-05-17T23:49:48.781012+00:00"},{"alias_kind":"pith_short_12","alias_value":"2TZZSNLDISQZ","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"2TZZSNLDISQZSCZG","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"2TZZSNLD","created_at":"2026-05-18T12:33:07.085635+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/2TZZSNLDISQZSCZGF4ZV2YIC7P","json":"https://pith.science/pith/2TZZSNLDISQZSCZGF4ZV2YIC7P.json","graph_json":"https://pith.science/api/pith-number/2TZZSNLDISQZSCZGF4ZV2YIC7P/graph.json","events_json":"https://pith.science/api/pith-number/2TZZSNLDISQZSCZGF4ZV2YIC7P/events.json","paper":"https://pith.science/paper/2TZZSNLD"},"agent_actions":{"view_html":"https://pith.science/pith/2TZZSNLDISQZSCZGF4ZV2YIC7P","download_json":"https://pith.science/pith/2TZZSNLDISQZSCZGF4ZV2YIC7P.json","view_paper":"https://pith.science/paper/2TZZSNLD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.00320&json=true","fetch_graph":"https://pith.science/api/pith-number/2TZZSNLDISQZSCZGF4ZV2YIC7P/graph.json","fetch_events":"https://pith.science/api/pith-number/2TZZSNLDISQZSCZGF4ZV2YIC7P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2TZZSNLDISQZSCZGF4ZV2YIC7P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2TZZSNLDISQZSCZGF4ZV2YIC7P/action/storage_attestation","attest_author":"https://pith.science/pith/2TZZSNLDISQZSCZGF4ZV2YIC7P/action/author_attestation","sign_citation":"https://pith.science/pith/2TZZSNLDISQZSCZGF4ZV2YIC7P/action/citation_signature","submit_replication":"https://pith.science/pith/2TZZSNLDISQZSCZGF4ZV2YIC7P/action/replication_record"}},"created_at":"2026-05-17T23:49:48.781012+00:00","updated_at":"2026-05-17T23:49:48.781012+00:00"}