{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:64LRMI46UMER27MCHFYVBTKPRH","short_pith_number":"pith:64LRMI46","schema_version":"1.0","canonical_sha256":"f71716239ea3091d7d82397150cd4f89ce2e6ccfaa9a0854f4d73004ea2107af","source":{"kind":"arxiv","id":"1803.06189","version":1},"attestation_state":"computed","paper":{"title":"Triplet-Center Loss for Multi-View 3D Object Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Song Bai, Xiang Bai, Xinwei He, Yang Zhou, Zhichao Zhou","submitted_at":"2018-03-16T12:31:24Z","abstract_excerpt":"Most existing 3D object recognition algorithms focus on leveraging the strong discriminative power of deep learning models with softmax loss for the classification of 3D data, while learning discriminative features with deep metric learning for 3D object retrieval is more or less neglected. In the paper, we study variants of deep metric learning losses for 3D object retrieval, which did not receive enough attention from this area. First , two kinds of representative losses, triplet loss and center loss, are introduced which could learn more discriminative features than traditional classificati"},"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":"1803.06189","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-16T12:31:24Z","cross_cats_sorted":[],"title_canon_sha256":"ff853b16f7b3d79216201e982c52c8d5d3d193a0641fd891c78b1223a6e0beb6","abstract_canon_sha256":"7ccf342c515a0a42a848ebd5fcd666db71258344b6d4c8c33c7a8b56b6536c31"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:50.332525Z","signature_b64":"nx7oPKUuWCJoIJ1yOhjprHCw3rDu7IxvGWnVYEas772WoYIxFByRr4Uc6tbZOaVu6blc0vy7e9XSX6grNVoeAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f71716239ea3091d7d82397150cd4f89ce2e6ccfaa9a0854f4d73004ea2107af","last_reissued_at":"2026-05-18T00:20:50.332014Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:50.332014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Triplet-Center Loss for Multi-View 3D Object Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Song Bai, Xiang Bai, Xinwei He, Yang Zhou, Zhichao Zhou","submitted_at":"2018-03-16T12:31:24Z","abstract_excerpt":"Most existing 3D object recognition algorithms focus on leveraging the strong discriminative power of deep learning models with softmax loss for the classification of 3D data, while learning discriminative features with deep metric learning for 3D object retrieval is more or less neglected. In the paper, we study variants of deep metric learning losses for 3D object retrieval, which did not receive enough attention from this area. First , two kinds of representative losses, triplet loss and center loss, are introduced which could learn more discriminative features than traditional classificati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.06189","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":"1803.06189","created_at":"2026-05-18T00:20:50.332095+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.06189v1","created_at":"2026-05-18T00:20:50.332095+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.06189","created_at":"2026-05-18T00:20:50.332095+00:00"},{"alias_kind":"pith_short_12","alias_value":"64LRMI46UMER","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"64LRMI46UMER27MC","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"64LRMI46","created_at":"2026-05-18T12:32:08.215937+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/64LRMI46UMER27MCHFYVBTKPRH","json":"https://pith.science/pith/64LRMI46UMER27MCHFYVBTKPRH.json","graph_json":"https://pith.science/api/pith-number/64LRMI46UMER27MCHFYVBTKPRH/graph.json","events_json":"https://pith.science/api/pith-number/64LRMI46UMER27MCHFYVBTKPRH/events.json","paper":"https://pith.science/paper/64LRMI46"},"agent_actions":{"view_html":"https://pith.science/pith/64LRMI46UMER27MCHFYVBTKPRH","download_json":"https://pith.science/pith/64LRMI46UMER27MCHFYVBTKPRH.json","view_paper":"https://pith.science/paper/64LRMI46","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.06189&json=true","fetch_graph":"https://pith.science/api/pith-number/64LRMI46UMER27MCHFYVBTKPRH/graph.json","fetch_events":"https://pith.science/api/pith-number/64LRMI46UMER27MCHFYVBTKPRH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/64LRMI46UMER27MCHFYVBTKPRH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/64LRMI46UMER27MCHFYVBTKPRH/action/storage_attestation","attest_author":"https://pith.science/pith/64LRMI46UMER27MCHFYVBTKPRH/action/author_attestation","sign_citation":"https://pith.science/pith/64LRMI46UMER27MCHFYVBTKPRH/action/citation_signature","submit_replication":"https://pith.science/pith/64LRMI46UMER27MCHFYVBTKPRH/action/replication_record"}},"created_at":"2026-05-18T00:20:50.332095+00:00","updated_at":"2026-05-18T00:20:50.332095+00:00"}