{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4HVZWG6G4JS6AXEZUZ7XNARNAU","short_pith_number":"pith:4HVZWG6G","schema_version":"1.0","canonical_sha256":"e1eb9b1bc6e265e05c99a67f76822d0522e0f953c16bdb2442eb1c818c1aab04","source":{"kind":"arxiv","id":"2307.09004","version":2},"attestation_state":"computed","paper":{"title":"Ord2Seq: Regarding Ordinal Regression as Label Sequence Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.AI","authors_text":"Danny Chen, Jian Wu, Jinhong Wang, Jintai Chen, Tingting Chen, Yi Cheng","submitted_at":"2023-07-18T06:44:20Z","abstract_excerpt":"Ordinal regression refers to classifying object instances into ordinal categories. It has been widely studied in many scenarios, such as medical disease grading, movie rating, etc. Known methods focused only on learning inter-class ordinal relationships, but still incur limitations in distinguishing adjacent categories thus far. In this paper, we propose a simple sequence prediction framework for ordinal regression called Ord2Seq, which, for the first time, transforms each ordinal category label into a special label sequence and thus regards an ordinal regression task as a sequence prediction "},"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":"2307.09004","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-07-18T06:44:20Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"b7a348a169a4bdb31388ca32e5402f7e28fc1c49b5ebd7474e4532020889bdbe","abstract_canon_sha256":"b1b54ddc2087a36225906d51f6f6a1ae4567c7d72b2af299d3a6801f13521ddd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:33:22.403940Z","signature_b64":"xf26101DePaFZDfDIj/jHn2UVxCjyXQCirvHpSJ41PHVj2FCVqk0AjxpgVCVLEwuCyTuCgWz2z8dOM4AQ5N+Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1eb9b1bc6e265e05c99a67f76822d0522e0f953c16bdb2442eb1c818c1aab04","last_reissued_at":"2026-07-05T06:33:22.403501Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:33:22.403501Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ord2Seq: Regarding Ordinal Regression as Label Sequence Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.AI","authors_text":"Danny Chen, Jian Wu, Jinhong Wang, Jintai Chen, Tingting Chen, Yi Cheng","submitted_at":"2023-07-18T06:44:20Z","abstract_excerpt":"Ordinal regression refers to classifying object instances into ordinal categories. It has been widely studied in many scenarios, such as medical disease grading, movie rating, etc. Known methods focused only on learning inter-class ordinal relationships, but still incur limitations in distinguishing adjacent categories thus far. In this paper, we propose a simple sequence prediction framework for ordinal regression called Ord2Seq, which, for the first time, transforms each ordinal category label into a special label sequence and thus regards an ordinal regression task as a sequence prediction "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.09004","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/2307.09004/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":"2307.09004","created_at":"2026-07-05T06:33:22.403559+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.09004v2","created_at":"2026-07-05T06:33:22.403559+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.09004","created_at":"2026-07-05T06:33:22.403559+00:00"},{"alias_kind":"pith_short_12","alias_value":"4HVZWG6G4JS6","created_at":"2026-07-05T06:33:22.403559+00:00"},{"alias_kind":"pith_short_16","alias_value":"4HVZWG6G4JS6AXEZ","created_at":"2026-07-05T06:33:22.403559+00:00"},{"alias_kind":"pith_short_8","alias_value":"4HVZWG6G","created_at":"2026-07-05T06:33:22.403559+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/4HVZWG6G4JS6AXEZUZ7XNARNAU","json":"https://pith.science/pith/4HVZWG6G4JS6AXEZUZ7XNARNAU.json","graph_json":"https://pith.science/api/pith-number/4HVZWG6G4JS6AXEZUZ7XNARNAU/graph.json","events_json":"https://pith.science/api/pith-number/4HVZWG6G4JS6AXEZUZ7XNARNAU/events.json","paper":"https://pith.science/paper/4HVZWG6G"},"agent_actions":{"view_html":"https://pith.science/pith/4HVZWG6G4JS6AXEZUZ7XNARNAU","download_json":"https://pith.science/pith/4HVZWG6G4JS6AXEZUZ7XNARNAU.json","view_paper":"https://pith.science/paper/4HVZWG6G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.09004&json=true","fetch_graph":"https://pith.science/api/pith-number/4HVZWG6G4JS6AXEZUZ7XNARNAU/graph.json","fetch_events":"https://pith.science/api/pith-number/4HVZWG6G4JS6AXEZUZ7XNARNAU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4HVZWG6G4JS6AXEZUZ7XNARNAU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4HVZWG6G4JS6AXEZUZ7XNARNAU/action/storage_attestation","attest_author":"https://pith.science/pith/4HVZWG6G4JS6AXEZUZ7XNARNAU/action/author_attestation","sign_citation":"https://pith.science/pith/4HVZWG6G4JS6AXEZUZ7XNARNAU/action/citation_signature","submit_replication":"https://pith.science/pith/4HVZWG6G4JS6AXEZUZ7XNARNAU/action/replication_record"}},"created_at":"2026-07-05T06:33:22.403559+00:00","updated_at":"2026-07-05T06:33:22.403559+00:00"}