{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:NI2PIYXMQDM7EX7OHG72BT3GCJ","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":"da9883566271e7b23b4843ab6875f0bd92d51624ce15eec4fd7fab40e1a2119a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-16T11:46:16Z","title_canon_sha256":"9a2c1cc4abc6d505ffd556456dbd8df2038cfda2acf252300f9c5de777a03ed5"},"schema_version":"1.0","source":{"id":"1711.06045","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.06045","created_at":"2026-05-17T23:52:44Z"},{"alias_kind":"arxiv_version","alias_value":"1711.06045v2","created_at":"2026-05-17T23:52:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.06045","created_at":"2026-05-17T23:52:44Z"},{"alias_kind":"pith_short_12","alias_value":"NI2PIYXMQDM7","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"NI2PIYXMQDM7EX7O","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"NI2PIYXM","created_at":"2026-05-18T12:31:31Z"}],"graph_snapshots":[{"event_id":"sha256:241b8ad65d2ad124fe2d741a2a6627c5030052557f55c77363062d04a5479c19","target":"graph","created_at":"2026-05-17T23:52:44Z","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":"Frame interpolation attempts to synthesise frames given one or more consecutive video frames. In recent years, deep learning approaches, and notably convolutional neural networks, have succeeded at tackling low- and high-level computer vision problems including frame interpolation. These techniques often tackle two problems, namely algorithm efficiency and reconstruction quality. In this paper, we present a multi-scale generative adversarial network for frame interpolation (\\mbox{FIGAN}). To maximise the efficiency of our network, we propose a novel multi-scale residual estimation module where","authors_text":"Alejandro Acosta, Francisco Massa, Johannes Totz, Joost van Amersfoort, Jose Caballero, Wenzhe Shi, Zehan Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-16T11:46:16Z","title":"Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.06045","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":"sha256:5b5ff5d47d7da6b0dbb2a69862f6a95f98171b1482a701f1fee0ab5276c67503","target":"record","created_at":"2026-05-17T23:52:44Z","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":"da9883566271e7b23b4843ab6875f0bd92d51624ce15eec4fd7fab40e1a2119a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-16T11:46:16Z","title_canon_sha256":"9a2c1cc4abc6d505ffd556456dbd8df2038cfda2acf252300f9c5de777a03ed5"},"schema_version":"1.0","source":{"id":"1711.06045","kind":"arxiv","version":2}},"canonical_sha256":"6a34f462ec80d9f25fee39bfa0cf66124859ae1315eb8ca50a0c19bb10dbe891","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6a34f462ec80d9f25fee39bfa0cf66124859ae1315eb8ca50a0c19bb10dbe891","first_computed_at":"2026-05-17T23:52:44.430680Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:44.430680Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"R1fLff6On7fzFV0G0mKGU8koLxS4bIYEBWM4Ng4wuf9tHywHFhOcdH0aGQ8iISYlGC5zCKDDZnsWbImYNRX1Bg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:44.431096Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.06045","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5b5ff5d47d7da6b0dbb2a69862f6a95f98171b1482a701f1fee0ab5276c67503","sha256:241b8ad65d2ad124fe2d741a2a6627c5030052557f55c77363062d04a5479c19"],"state_sha256":"114af85e9466a84be3c7086559f05f4dd6ec31e1d17930b61dd74082378dfb25"}