{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:RSVXZBOZ4DGQ5A7QMNOHU6KIVF","short_pith_number":"pith:RSVXZBOZ","canonical_record":{"source":{"id":"1907.11628","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-26T15:28:59Z","cross_cats_sorted":[],"title_canon_sha256":"1a096cde8b6f43ffdaf7f31a43e46b43a36a7bc1b017b855f4d6b39c8774be70","abstract_canon_sha256":"da8ae269ef434886d0c19f300c2bdd81330f9cba42ab767d8a4bb301c25adba0"},"schema_version":"1.0"},"canonical_sha256":"8cab7c85d9e0cd0e83f0635c7a7948a9555cda3c2ee96199902c171ccb7af47c","source":{"kind":"arxiv","id":"1907.11628","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.11628","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"arxiv_version","alias_value":"1907.11628v1","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.11628","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"pith_short_12","alias_value":"RSVXZBOZ4DGQ","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"RSVXZBOZ4DGQ5A7Q","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"RSVXZBOZ","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:RSVXZBOZ4DGQ5A7QMNOHU6KIVF","target":"record","payload":{"canonical_record":{"source":{"id":"1907.11628","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-26T15:28:59Z","cross_cats_sorted":[],"title_canon_sha256":"1a096cde8b6f43ffdaf7f31a43e46b43a36a7bc1b017b855f4d6b39c8774be70","abstract_canon_sha256":"da8ae269ef434886d0c19f300c2bdd81330f9cba42ab767d8a4bb301c25adba0"},"schema_version":"1.0"},"canonical_sha256":"8cab7c85d9e0cd0e83f0635c7a7948a9555cda3c2ee96199902c171ccb7af47c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:28.480240Z","signature_b64":"ZfOf5Pc0IK4M4MnZ9BFp/mRwNuc9jVVyKOF6LdzZLIDeGFVNPuX7TJSScXIlKkIZrF2zsgul5jgbtMNcOaLSDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8cab7c85d9e0cd0e83f0635c7a7948a9555cda3c2ee96199902c171ccb7af47c","last_reissued_at":"2026-05-17T23:39:28.479639Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:28.479639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.11628","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M4JJXvdIYQBX0rwiU77rDtfwEb5JU9wDFz9UhU8XrgD164hAazyoaYUMwWMzJvygpRXjhF42ZrRnIBHKjpBKCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T04:55:24.677860Z"},"content_sha256":"d76fe5d6d321ea6652fa95ac087a55958f94b9893108ef731a6675c4ecc4ed6f","schema_version":"1.0","event_id":"sha256:d76fe5d6d321ea6652fa95ac087a55958f94b9893108ef731a6675c4ecc4ed6f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:RSVXZBOZ4DGQ5A7QMNOHU6KIVF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Learning for Optical Flow Estimation Using Pyramid Convolution LSTM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haoxin Li, Shuosen Guan, Wei-Shi Zheng","submitted_at":"2019-07-26T15:28:59Z","abstract_excerpt":"Most of current Convolution Neural Network (CNN) based methods for optical flow estimation focus on learning optical flow on synthetic datasets with groundtruth, which is not practical. In this paper, we propose an unsupervised optical flow estimation framework named PCLNet. It uses pyramid Convolution LSTM (ConvLSTM) with the constraint of adjacent frame reconstruction, which allows flexibly estimating multi-frame optical flows from any video clip. Besides, by decoupling motion feature learning and optical flow representation, our method avoids complex short-cut connections used in existing f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.11628","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":""},"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":"2026-05-17T23:39:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s7SOMd6aaFmOQhlZI7xhFr8D3j9jFhnFUYHIrEU8U3Fgf9HO6RgW0JPwq3wi2cuNYFb72y+Ome9oeAO6K4+nBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T04:55:24.678518Z"},"content_sha256":"18bc7225f8aecb6ed99062767a4bebd91aae68256c46f16517c18f47132f0513","schema_version":"1.0","event_id":"sha256:18bc7225f8aecb6ed99062767a4bebd91aae68256c46f16517c18f47132f0513"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RSVXZBOZ4DGQ5A7QMNOHU6KIVF/bundle.json","state_url":"https://pith.science/pith/RSVXZBOZ4DGQ5A7QMNOHU6KIVF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RSVXZBOZ4DGQ5A7QMNOHU6KIVF/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":"2026-05-27T04:55:24Z","links":{"resolver":"https://pith.science/pith/RSVXZBOZ4DGQ5A7QMNOHU6KIVF","bundle":"https://pith.science/pith/RSVXZBOZ4DGQ5A7QMNOHU6KIVF/bundle.json","state":"https://pith.science/pith/RSVXZBOZ4DGQ5A7QMNOHU6KIVF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RSVXZBOZ4DGQ5A7QMNOHU6KIVF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:RSVXZBOZ4DGQ5A7QMNOHU6KIVF","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":"da8ae269ef434886d0c19f300c2bdd81330f9cba42ab767d8a4bb301c25adba0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-26T15:28:59Z","title_canon_sha256":"1a096cde8b6f43ffdaf7f31a43e46b43a36a7bc1b017b855f4d6b39c8774be70"},"schema_version":"1.0","source":{"id":"1907.11628","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.11628","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"arxiv_version","alias_value":"1907.11628v1","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.11628","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"pith_short_12","alias_value":"RSVXZBOZ4DGQ","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"RSVXZBOZ4DGQ5A7Q","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"RSVXZBOZ","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:18bc7225f8aecb6ed99062767a4bebd91aae68256c46f16517c18f47132f0513","target":"graph","created_at":"2026-05-17T23:39:28Z","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"},"paper":{"abstract_excerpt":"Most of current Convolution Neural Network (CNN) based methods for optical flow estimation focus on learning optical flow on synthetic datasets with groundtruth, which is not practical. In this paper, we propose an unsupervised optical flow estimation framework named PCLNet. It uses pyramid Convolution LSTM (ConvLSTM) with the constraint of adjacent frame reconstruction, which allows flexibly estimating multi-frame optical flows from any video clip. Besides, by decoupling motion feature learning and optical flow representation, our method avoids complex short-cut connections used in existing f","authors_text":"Haoxin Li, Shuosen Guan, Wei-Shi Zheng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-26T15:28:59Z","title":"Unsupervised Learning for Optical Flow Estimation Using Pyramid Convolution LSTM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.11628","kind":"arxiv","version":1},"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:d76fe5d6d321ea6652fa95ac087a55958f94b9893108ef731a6675c4ecc4ed6f","target":"record","created_at":"2026-05-17T23:39:28Z","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":"da8ae269ef434886d0c19f300c2bdd81330f9cba42ab767d8a4bb301c25adba0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-26T15:28:59Z","title_canon_sha256":"1a096cde8b6f43ffdaf7f31a43e46b43a36a7bc1b017b855f4d6b39c8774be70"},"schema_version":"1.0","source":{"id":"1907.11628","kind":"arxiv","version":1}},"canonical_sha256":"8cab7c85d9e0cd0e83f0635c7a7948a9555cda3c2ee96199902c171ccb7af47c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8cab7c85d9e0cd0e83f0635c7a7948a9555cda3c2ee96199902c171ccb7af47c","first_computed_at":"2026-05-17T23:39:28.479639Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:28.479639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZfOf5Pc0IK4M4MnZ9BFp/mRwNuc9jVVyKOF6LdzZLIDeGFVNPuX7TJSScXIlKkIZrF2zsgul5jgbtMNcOaLSDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:28.480240Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.11628","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d76fe5d6d321ea6652fa95ac087a55958f94b9893108ef731a6675c4ecc4ed6f","sha256:18bc7225f8aecb6ed99062767a4bebd91aae68256c46f16517c18f47132f0513"],"state_sha256":"eccb60c706dd51fee485f826bc0d5d1c55b7fd8f87c9ef60c2bd6dfde6d5181f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E0DzknihTA+3j2gpFFF/3nYvyq5iDWohM27+mrMgreOEjYv/uqzxPUousXjeTKuKTYKjV0i5xCk7LV7cQbhHDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T04:55:24.681894Z","bundle_sha256":"3facbba29c222bd57d9f0d548a69751590cb0d9d8e1983946837ab4a393e7526"}}