{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:BBJDZUV3ICINH74P43KJZQD3RD","short_pith_number":"pith:BBJDZUV3","canonical_record":{"source":{"id":"2412.04930","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-06T10:35:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1c0b4663c9e8d503412f88c0db10c9d63b0444a9914516d9eb7ca87a73e9313e","abstract_canon_sha256":"c348222474bc3b41bf367ee8b67c0829a1bf446bf321f7cc2f10222ca3a2ba2a"},"schema_version":"1.0"},"canonical_sha256":"08523cd2bb4090d3ff8fe6d49cc07b88c41664fca1b02086816548f16cbf0e2a","source":{"kind":"arxiv","id":"2412.04930","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.04930","created_at":"2026-07-05T09:46:00Z"},{"alias_kind":"arxiv_version","alias_value":"2412.04930v2","created_at":"2026-07-05T09:46:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04930","created_at":"2026-07-05T09:46:00Z"},{"alias_kind":"pith_short_12","alias_value":"BBJDZUV3ICIN","created_at":"2026-07-05T09:46:00Z"},{"alias_kind":"pith_short_16","alias_value":"BBJDZUV3ICINH74P","created_at":"2026-07-05T09:46:00Z"},{"alias_kind":"pith_short_8","alias_value":"BBJDZUV3","created_at":"2026-07-05T09:46:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:BBJDZUV3ICINH74P43KJZQD3RD","target":"record","payload":{"canonical_record":{"source":{"id":"2412.04930","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-06T10:35:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1c0b4663c9e8d503412f88c0db10c9d63b0444a9914516d9eb7ca87a73e9313e","abstract_canon_sha256":"c348222474bc3b41bf367ee8b67c0829a1bf446bf321f7cc2f10222ca3a2ba2a"},"schema_version":"1.0"},"canonical_sha256":"08523cd2bb4090d3ff8fe6d49cc07b88c41664fca1b02086816548f16cbf0e2a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:46:00.607502Z","signature_b64":"isWNODFD2qQDxob2oxrnPbLA3MLnfKdFqrQ6ZiJM4gNQITGwDcC9m7qavCTPo41HjP/ez2kW4U333XFzxMgQDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"08523cd2bb4090d3ff8fe6d49cc07b88c41664fca1b02086816548f16cbf0e2a","last_reissued_at":"2026-07-05T09:46:00.607081Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:46:00.607081Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.04930","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:46:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7MxmLE5XCYjOccwUDI6dSnyWbJzkkiWz/AxurP6R3A+ci6OUWo9Q6HnF3dxR8wqaK2Np4gG7iZWZMcCemjjnCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T09:34:45.684527Z"},"content_sha256":"d838db54720f8ed817132f2f10d95e28bef2779441ef04cfc8d84a5fd206261b","schema_version":"1.0","event_id":"sha256:d838db54720f8ed817132f2f10d95e28bef2779441ef04cfc8d84a5fd206261b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:BBJDZUV3ICINH74P43KJZQD3RD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Video Decomposition Prior: A Methodology to Decompose Videos into Layers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Abhinav Shrivastava, Gaurav Shrivastava, Ser-Nam Lim","submitted_at":"2024-12-06T10:35:45Z","abstract_excerpt":"In the evolving landscape of video enhancement and editing methodologies, a majority of deep learning techniques often rely on extensive datasets of observed input and ground truth sequence pairs for optimal performance. Such reliance often falters when acquiring data becomes challenging, especially in tasks like video dehazing and relighting, where replicating identical motions and camera angles in both corrupted and ground truth sequences is complicated. Moreover, these conventional methodologies perform best when the test distribution closely mirrors the training distribution. Recognizing t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04930","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/2412.04930/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:46:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LteDTGiDfhThPQzEU/gtf5PVDVemmwz52V4YlkX85+yk72N72agMq6RTHkFbhSzJZJaA12RI/w+xG1WCcpjcCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T09:34:45.742189Z"},"content_sha256":"5f444ebeb9ee3ac6f92bfb691f27af018e20c5e162e36fdfda2974421477384f","schema_version":"1.0","event_id":"sha256:5f444ebeb9ee3ac6f92bfb691f27af018e20c5e162e36fdfda2974421477384f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BBJDZUV3ICINH74P43KJZQD3RD/bundle.json","state_url":"https://pith.science/pith/BBJDZUV3ICINH74P43KJZQD3RD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BBJDZUV3ICINH74P43KJZQD3RD/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-14T09:34:45Z","links":{"resolver":"https://pith.science/pith/BBJDZUV3ICINH74P43KJZQD3RD","bundle":"https://pith.science/pith/BBJDZUV3ICINH74P43KJZQD3RD/bundle.json","state":"https://pith.science/pith/BBJDZUV3ICINH74P43KJZQD3RD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BBJDZUV3ICINH74P43KJZQD3RD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:BBJDZUV3ICINH74P43KJZQD3RD","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c348222474bc3b41bf367ee8b67c0829a1bf446bf321f7cc2f10222ca3a2ba2a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-06T10:35:45Z","title_canon_sha256":"1c0b4663c9e8d503412f88c0db10c9d63b0444a9914516d9eb7ca87a73e9313e"},"schema_version":"1.0","source":{"id":"2412.04930","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.04930","created_at":"2026-07-05T09:46:00Z"},{"alias_kind":"arxiv_version","alias_value":"2412.04930v2","created_at":"2026-07-05T09:46:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04930","created_at":"2026-07-05T09:46:00Z"},{"alias_kind":"pith_short_12","alias_value":"BBJDZUV3ICIN","created_at":"2026-07-05T09:46:00Z"},{"alias_kind":"pith_short_16","alias_value":"BBJDZUV3ICINH74P","created_at":"2026-07-05T09:46:00Z"},{"alias_kind":"pith_short_8","alias_value":"BBJDZUV3","created_at":"2026-07-05T09:46:00Z"}],"graph_snapshots":[{"event_id":"sha256:5f444ebeb9ee3ac6f92bfb691f27af018e20c5e162e36fdfda2974421477384f","target":"graph","created_at":"2026-07-05T09:46:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2412.04930/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the evolving landscape of video enhancement and editing methodologies, a majority of deep learning techniques often rely on extensive datasets of observed input and ground truth sequence pairs for optimal performance. Such reliance often falters when acquiring data becomes challenging, especially in tasks like video dehazing and relighting, where replicating identical motions and camera angles in both corrupted and ground truth sequences is complicated. Moreover, these conventional methodologies perform best when the test distribution closely mirrors the training distribution. Recognizing t","authors_text":"Abhinav Shrivastava, Gaurav Shrivastava, Ser-Nam Lim","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-06T10:35:45Z","title":"Video Decomposition Prior: A Methodology to Decompose Videos into Layers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04930","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d838db54720f8ed817132f2f10d95e28bef2779441ef04cfc8d84a5fd206261b","target":"record","created_at":"2026-07-05T09:46:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c348222474bc3b41bf367ee8b67c0829a1bf446bf321f7cc2f10222ca3a2ba2a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-06T10:35:45Z","title_canon_sha256":"1c0b4663c9e8d503412f88c0db10c9d63b0444a9914516d9eb7ca87a73e9313e"},"schema_version":"1.0","source":{"id":"2412.04930","kind":"arxiv","version":2}},"canonical_sha256":"08523cd2bb4090d3ff8fe6d49cc07b88c41664fca1b02086816548f16cbf0e2a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"08523cd2bb4090d3ff8fe6d49cc07b88c41664fca1b02086816548f16cbf0e2a","first_computed_at":"2026-07-05T09:46:00.607081Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:46:00.607081Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"isWNODFD2qQDxob2oxrnPbLA3MLnfKdFqrQ6ZiJM4gNQITGwDcC9m7qavCTPo41HjP/ez2kW4U333XFzxMgQDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:46:00.607502Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.04930","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d838db54720f8ed817132f2f10d95e28bef2779441ef04cfc8d84a5fd206261b","sha256:5f444ebeb9ee3ac6f92bfb691f27af018e20c5e162e36fdfda2974421477384f"],"state_sha256":"41e18d39a3e58aa57df3070b3eeb134b8e237d27fd7ee17e581e6f03018b6e2f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F/OKkOKQmp/ekCMZ4nr/+beAX4fSgpQCaqV+AIkaIyOXrTDZS/kKnMM1FCknf93CG+X9et9bz+EHKHH5GbVhAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T09:34:45.749416Z","bundle_sha256":"7d0b6a9d75aba7c9bac13c7404df65f9b4cd961eb7d3d4a4128a7612dc2d382d"}}