{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WQ442AVSFUN2ODZQDTHCSR4DFY","short_pith_number":"pith:WQ442AVS","schema_version":"1.0","canonical_sha256":"b439cd02b22d1ba70f301cce2947832e25d66680a59e1f9454185def1d25f068","source":{"kind":"arxiv","id":"2406.05513","version":2},"attestation_state":"computed","paper":{"title":"A Two-Stage Adverse Weather Semantic Segmentation Method for WeatherProof Challenge CVPR 2024 Workshop UG2+","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dehua Hu, Jianzhao Wang, Kun Li, Shengeng Tang, Yanyan Wei, Yilin Zhang, Zhao Zhang","submitted_at":"2024-06-08T16:22:26Z","abstract_excerpt":"This technical report presents our team's solution for the WeatherProof Dataset Challenge: Semantic Segmentation in Adverse Weather at CVPR'24 UG2+. We propose a two-stage deep learning framework for this task. In the first stage, we preprocess the provided dataset by concatenating images into video sequences. Subsequently, we leverage a low-rank video deraining method to generate high-fidelity pseudo ground truths. These pseudo ground truths offer superior alignment compared to the original ground truths, facilitating model convergence during training. In the second stage, we employ the Inter"},"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":"2406.05513","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-08T16:22:26Z","cross_cats_sorted":[],"title_canon_sha256":"b14a1cf2d13d61084663ca5fe77a040d7d23879cd2e064921721cae528567569","abstract_canon_sha256":"407381ecb07fef21fc37492ef6896edd5bec156a43e9b90d6c86b214a28c01c7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:42:36.847985Z","signature_b64":"OKvD8JZCfb6QqNUr1eUC0AfhpUSWhOCgF5l7RaYpaDBDdQDwXPeZ0DYGu+urj2x+CqhQDFO8O30PMdnagU2kAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b439cd02b22d1ba70f301cce2947832e25d66680a59e1f9454185def1d25f068","last_reissued_at":"2026-07-05T08:42:36.847559Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:42:36.847559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Two-Stage Adverse Weather Semantic Segmentation Method for WeatherProof Challenge CVPR 2024 Workshop UG2+","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dehua Hu, Jianzhao Wang, Kun Li, Shengeng Tang, Yanyan Wei, Yilin Zhang, Zhao Zhang","submitted_at":"2024-06-08T16:22:26Z","abstract_excerpt":"This technical report presents our team's solution for the WeatherProof Dataset Challenge: Semantic Segmentation in Adverse Weather at CVPR'24 UG2+. We propose a two-stage deep learning framework for this task. In the first stage, we preprocess the provided dataset by concatenating images into video sequences. Subsequently, we leverage a low-rank video deraining method to generate high-fidelity pseudo ground truths. These pseudo ground truths offer superior alignment compared to the original ground truths, facilitating model convergence during training. In the second stage, we employ the Inter"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05513","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/2406.05513/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":"2406.05513","created_at":"2026-07-05T08:42:36.847616+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.05513v2","created_at":"2026-07-05T08:42:36.847616+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05513","created_at":"2026-07-05T08:42:36.847616+00:00"},{"alias_kind":"pith_short_12","alias_value":"WQ442AVSFUN2","created_at":"2026-07-05T08:42:36.847616+00:00"},{"alias_kind":"pith_short_16","alias_value":"WQ442AVSFUN2ODZQ","created_at":"2026-07-05T08:42:36.847616+00:00"},{"alias_kind":"pith_short_8","alias_value":"WQ442AVS","created_at":"2026-07-05T08:42:36.847616+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/WQ442AVSFUN2ODZQDTHCSR4DFY","json":"https://pith.science/pith/WQ442AVSFUN2ODZQDTHCSR4DFY.json","graph_json":"https://pith.science/api/pith-number/WQ442AVSFUN2ODZQDTHCSR4DFY/graph.json","events_json":"https://pith.science/api/pith-number/WQ442AVSFUN2ODZQDTHCSR4DFY/events.json","paper":"https://pith.science/paper/WQ442AVS"},"agent_actions":{"view_html":"https://pith.science/pith/WQ442AVSFUN2ODZQDTHCSR4DFY","download_json":"https://pith.science/pith/WQ442AVSFUN2ODZQDTHCSR4DFY.json","view_paper":"https://pith.science/paper/WQ442AVS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.05513&json=true","fetch_graph":"https://pith.science/api/pith-number/WQ442AVSFUN2ODZQDTHCSR4DFY/graph.json","fetch_events":"https://pith.science/api/pith-number/WQ442AVSFUN2ODZQDTHCSR4DFY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WQ442AVSFUN2ODZQDTHCSR4DFY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WQ442AVSFUN2ODZQDTHCSR4DFY/action/storage_attestation","attest_author":"https://pith.science/pith/WQ442AVSFUN2ODZQDTHCSR4DFY/action/author_attestation","sign_citation":"https://pith.science/pith/WQ442AVSFUN2ODZQDTHCSR4DFY/action/citation_signature","submit_replication":"https://pith.science/pith/WQ442AVSFUN2ODZQDTHCSR4DFY/action/replication_record"}},"created_at":"2026-07-05T08:42:36.847616+00:00","updated_at":"2026-07-05T08:42:36.847616+00:00"}