{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UWQC66ODZFF4UEEG7QWOULA3E4","short_pith_number":"pith:UWQC66OD","schema_version":"1.0","canonical_sha256":"a5a02f79c3c94bca1086fc2cea2c1b2701f5a995b9aa4ce240d5686f4bf819bc","source":{"kind":"arxiv","id":"2401.01448","version":2},"attestation_state":"computed","paper":{"title":"ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ahmad Sajedi, Konstantinos N. Plataniotis, Samir Khaki, Yuri A. Lawryshyn","submitted_at":"2024-01-02T22:15:20Z","abstract_excerpt":"Multi-label image classification presents a challenging task in many domains, including computer vision and medical imaging. Recent advancements have introduced graph-based and transformer-based methods to improve performance and capture label dependencies. However, these methods often include complex modules that entail heavy computation and lack interpretability. In this paper, we propose Probabilistic Multi-label Contrastive Learning (ProbMCL), a novel framework to address these challenges in multi-label image classification tasks. Our simple yet effective approach employs supervised contra"},"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":"2401.01448","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-02T22:15:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"767adc215efbcec82aa42de652a0396ddf23b8ac5013a1419867bde782eac710","abstract_canon_sha256":"8db8b1f1496f8086939470d2a7a45d30189b2d53e1af54e8946921e54e040325"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:07:12.870613Z","signature_b64":"h24otRaQo2zj8gXP1uy7yeKcNXubSFdkBYHAf9d7GzVplxBap4UGH+Mo6IVR5vgYG2IVJbb7pA9mQ/0cQfS3DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5a02f79c3c94bca1086fc2cea2c1b2701f5a995b9aa4ce240d5686f4bf819bc","last_reissued_at":"2026-07-05T08:07:12.870189Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:07:12.870189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ahmad Sajedi, Konstantinos N. Plataniotis, Samir Khaki, Yuri A. Lawryshyn","submitted_at":"2024-01-02T22:15:20Z","abstract_excerpt":"Multi-label image classification presents a challenging task in many domains, including computer vision and medical imaging. Recent advancements have introduced graph-based and transformer-based methods to improve performance and capture label dependencies. However, these methods often include complex modules that entail heavy computation and lack interpretability. In this paper, we propose Probabilistic Multi-label Contrastive Learning (ProbMCL), a novel framework to address these challenges in multi-label image classification tasks. Our simple yet effective approach employs supervised contra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.01448","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/2401.01448/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":"2401.01448","created_at":"2026-07-05T08:07:12.870247+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.01448v2","created_at":"2026-07-05T08:07:12.870247+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.01448","created_at":"2026-07-05T08:07:12.870247+00:00"},{"alias_kind":"pith_short_12","alias_value":"UWQC66ODZFF4","created_at":"2026-07-05T08:07:12.870247+00:00"},{"alias_kind":"pith_short_16","alias_value":"UWQC66ODZFF4UEEG","created_at":"2026-07-05T08:07:12.870247+00:00"},{"alias_kind":"pith_short_8","alias_value":"UWQC66OD","created_at":"2026-07-05T08:07:12.870247+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/UWQC66ODZFF4UEEG7QWOULA3E4","json":"https://pith.science/pith/UWQC66ODZFF4UEEG7QWOULA3E4.json","graph_json":"https://pith.science/api/pith-number/UWQC66ODZFF4UEEG7QWOULA3E4/graph.json","events_json":"https://pith.science/api/pith-number/UWQC66ODZFF4UEEG7QWOULA3E4/events.json","paper":"https://pith.science/paper/UWQC66OD"},"agent_actions":{"view_html":"https://pith.science/pith/UWQC66ODZFF4UEEG7QWOULA3E4","download_json":"https://pith.science/pith/UWQC66ODZFF4UEEG7QWOULA3E4.json","view_paper":"https://pith.science/paper/UWQC66OD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.01448&json=true","fetch_graph":"https://pith.science/api/pith-number/UWQC66ODZFF4UEEG7QWOULA3E4/graph.json","fetch_events":"https://pith.science/api/pith-number/UWQC66ODZFF4UEEG7QWOULA3E4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UWQC66ODZFF4UEEG7QWOULA3E4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UWQC66ODZFF4UEEG7QWOULA3E4/action/storage_attestation","attest_author":"https://pith.science/pith/UWQC66ODZFF4UEEG7QWOULA3E4/action/author_attestation","sign_citation":"https://pith.science/pith/UWQC66ODZFF4UEEG7QWOULA3E4/action/citation_signature","submit_replication":"https://pith.science/pith/UWQC66ODZFF4UEEG7QWOULA3E4/action/replication_record"}},"created_at":"2026-07-05T08:07:12.870247+00:00","updated_at":"2026-07-05T08:07:12.870247+00:00"}