{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6TRVPVKENJBF7L5HXI2XIAKWC2","short_pith_number":"pith:6TRVPVKE","schema_version":"1.0","canonical_sha256":"f4e357d5446a425fafa7ba35740156169ff960b6bb6267e50db68563b0dd99e6","source":{"kind":"arxiv","id":"2410.07901","version":1},"attestation_state":"computed","paper":{"title":"Semi-Supervised Video Desnowing Network via Temporal Decoupling Experts and Distribution-Driven Contrastive Regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Angelica I Aviles-Rivero, Haoyu Chen, Hongtao Wu, Jingjing Ren, Lei Zhu, Sixiang Chen, Yijun Yang","submitted_at":"2024-10-10T13:31:42Z","abstract_excerpt":"Snow degradations present formidable challenges to the advancement of computer vision tasks by the undesirable corruption in outdoor scenarios. While current deep learning-based desnowing approaches achieve success on synthetic benchmark datasets, they struggle to restore out-of-distribution real-world snowy videos due to the deficiency of paired real-world training data. To address this bottleneck, we devise a new paradigm for video desnowing in a semi-supervised spirit to involve unlabeled real data for the generalizable snow removal. Specifically, we construct a real-world dataset with 85 s"},"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":"2410.07901","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-10T13:31:42Z","cross_cats_sorted":[],"title_canon_sha256":"8da3c917920826c7c8c6adb4dd45f91217ea17c955c82958342ca69af4bfdfdf","abstract_canon_sha256":"e3770862f2a2ee3228a832a7928965393174be6fe3d0fa0faa9ad63e0ac95bdf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:18:45.934974Z","signature_b64":"wN596PQYzPv0BvcrV+YvsrPocbqcYFNAdKe4JWiyrGQnP6QeB1SJh5Z3YB18w1lFwVr9asQVP8rWtbLT04k4DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4e357d5446a425fafa7ba35740156169ff960b6bb6267e50db68563b0dd99e6","last_reissued_at":"2026-07-05T09:18:45.934551Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:18:45.934551Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semi-Supervised Video Desnowing Network via Temporal Decoupling Experts and Distribution-Driven Contrastive Regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Angelica I Aviles-Rivero, Haoyu Chen, Hongtao Wu, Jingjing Ren, Lei Zhu, Sixiang Chen, Yijun Yang","submitted_at":"2024-10-10T13:31:42Z","abstract_excerpt":"Snow degradations present formidable challenges to the advancement of computer vision tasks by the undesirable corruption in outdoor scenarios. While current deep learning-based desnowing approaches achieve success on synthetic benchmark datasets, they struggle to restore out-of-distribution real-world snowy videos due to the deficiency of paired real-world training data. To address this bottleneck, we devise a new paradigm for video desnowing in a semi-supervised spirit to involve unlabeled real data for the generalizable snow removal. Specifically, we construct a real-world dataset with 85 s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.07901","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/2410.07901/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":"2410.07901","created_at":"2026-07-05T09:18:45.934612+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.07901v1","created_at":"2026-07-05T09:18:45.934612+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.07901","created_at":"2026-07-05T09:18:45.934612+00:00"},{"alias_kind":"pith_short_12","alias_value":"6TRVPVKENJBF","created_at":"2026-07-05T09:18:45.934612+00:00"},{"alias_kind":"pith_short_16","alias_value":"6TRVPVKENJBF7L5H","created_at":"2026-07-05T09:18:45.934612+00:00"},{"alias_kind":"pith_short_8","alias_value":"6TRVPVKE","created_at":"2026-07-05T09:18:45.934612+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/6TRVPVKENJBF7L5HXI2XIAKWC2","json":"https://pith.science/pith/6TRVPVKENJBF7L5HXI2XIAKWC2.json","graph_json":"https://pith.science/api/pith-number/6TRVPVKENJBF7L5HXI2XIAKWC2/graph.json","events_json":"https://pith.science/api/pith-number/6TRVPVKENJBF7L5HXI2XIAKWC2/events.json","paper":"https://pith.science/paper/6TRVPVKE"},"agent_actions":{"view_html":"https://pith.science/pith/6TRVPVKENJBF7L5HXI2XIAKWC2","download_json":"https://pith.science/pith/6TRVPVKENJBF7L5HXI2XIAKWC2.json","view_paper":"https://pith.science/paper/6TRVPVKE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.07901&json=true","fetch_graph":"https://pith.science/api/pith-number/6TRVPVKENJBF7L5HXI2XIAKWC2/graph.json","fetch_events":"https://pith.science/api/pith-number/6TRVPVKENJBF7L5HXI2XIAKWC2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6TRVPVKENJBF7L5HXI2XIAKWC2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6TRVPVKENJBF7L5HXI2XIAKWC2/action/storage_attestation","attest_author":"https://pith.science/pith/6TRVPVKENJBF7L5HXI2XIAKWC2/action/author_attestation","sign_citation":"https://pith.science/pith/6TRVPVKENJBF7L5HXI2XIAKWC2/action/citation_signature","submit_replication":"https://pith.science/pith/6TRVPVKENJBF7L5HXI2XIAKWC2/action/replication_record"}},"created_at":"2026-07-05T09:18:45.934612+00:00","updated_at":"2026-07-05T09:18:45.934612+00:00"}