{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:6MGUETQUFLZY7HLAPJGQBWMNQQ","short_pith_number":"pith:6MGUETQU","canonical_record":{"source":{"id":"1812.09079","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-21T12:31:42Z","cross_cats_sorted":[],"title_canon_sha256":"229775981f94451cb2cb6b2810a771a8ee6dd8d9ac5ef919d5a95703275d0b51","abstract_canon_sha256":"06aa88c5682d8863bc25c9a15fa72efce1b2c7e7e928d25ff038a94fff727f78"},"schema_version":"1.0"},"canonical_sha256":"f30d424e142af38f9d607a4d00d98d841a52ab411c8d8639488fca111f28d304","source":{"kind":"arxiv","id":"1812.09079","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.09079","created_at":"2026-05-17T23:42:53Z"},{"alias_kind":"arxiv_version","alias_value":"1812.09079v2","created_at":"2026-05-17T23:42:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.09079","created_at":"2026-05-17T23:42:53Z"},{"alias_kind":"pith_short_12","alias_value":"6MGUETQUFLZY","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6MGUETQUFLZY7HLA","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6MGUETQU","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:6MGUETQUFLZY7HLAPJGQBWMNQQ","target":"record","payload":{"canonical_record":{"source":{"id":"1812.09079","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-21T12:31:42Z","cross_cats_sorted":[],"title_canon_sha256":"229775981f94451cb2cb6b2810a771a8ee6dd8d9ac5ef919d5a95703275d0b51","abstract_canon_sha256":"06aa88c5682d8863bc25c9a15fa72efce1b2c7e7e928d25ff038a94fff727f78"},"schema_version":"1.0"},"canonical_sha256":"f30d424e142af38f9d607a4d00d98d841a52ab411c8d8639488fca111f28d304","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:53.526605Z","signature_b64":"q07+bv7sUf6mjcFbCqFvFnyoBsOayRcRVCi9bCneDzhTHhGESwN1S7MUkzvkALh+kQwighdxzr24DTFhKbQRAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f30d424e142af38f9d607a4d00d98d841a52ab411c8d8639488fca111f28d304","last_reissued_at":"2026-05-17T23:42:53.525978Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:53.525978Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.09079","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":"2026-05-17T23:42:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gvy6QXd2BkZBUflIa11jSCn4ZCnQVAqbddUonCjUXmI172XPoq4f0324oT4UU0FEJSygvLHfQrzkpi/ddWl5Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T18:08:15.427477Z"},"content_sha256":"ad401ecd803f8a9d2e6b70c5f8c8a92d72d98c0cf02add5d15713760b86afa72","schema_version":"1.0","event_id":"sha256:ad401ecd803f8a9d2e6b70c5f8c8a92d72d98c0cf02add5d15713760b86afa72"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:6MGUETQUFLZY7HLAPJGQBWMNQQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"3DSRnet: Video Super-resolution using 3D Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jeongyeon Lim, Munchurl Kim, Soo Ye Kim, Taeyoung Na","submitted_at":"2018-12-21T12:31:42Z","abstract_excerpt":"In video super-resolution, the spatio-temporal coherence between, and among the frames must be exploited appropriately for accurate prediction of the high resolution frames. Although 2D convolutional neural networks (CNNs) are powerful in modelling images, 3D-CNNs are more suitable for spatio-temporal feature extraction as they can preserve temporal information. To this end, we propose an effective 3D-CNN for video super-resolution, called the 3DSRnet that does not require motion alignment as preprocessing. Our 3DSRnet maintains the temporal depth of spatio-temporal feature maps to maximally c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.09079","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":""},"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":"2026-05-17T23:42:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z056rJ32dVoyEeGxBpDgVnsdBu77Q8IlE0W8i8YCnGY29A9ipFbheGu26swI/KkODW0CzCV/RJWPaMApZhqLAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T18:08:15.427823Z"},"content_sha256":"1b173f49db8087c96c90d01af9585735478987a2fb97173431a2b9663488ed2a","schema_version":"1.0","event_id":"sha256:1b173f49db8087c96c90d01af9585735478987a2fb97173431a2b9663488ed2a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6MGUETQUFLZY7HLAPJGQBWMNQQ/bundle.json","state_url":"https://pith.science/pith/6MGUETQUFLZY7HLAPJGQBWMNQQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6MGUETQUFLZY7HLAPJGQBWMNQQ/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":"2026-05-30T18:08:15Z","links":{"resolver":"https://pith.science/pith/6MGUETQUFLZY7HLAPJGQBWMNQQ","bundle":"https://pith.science/pith/6MGUETQUFLZY7HLAPJGQBWMNQQ/bundle.json","state":"https://pith.science/pith/6MGUETQUFLZY7HLAPJGQBWMNQQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6MGUETQUFLZY7HLAPJGQBWMNQQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:6MGUETQUFLZY7HLAPJGQBWMNQQ","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":"06aa88c5682d8863bc25c9a15fa72efce1b2c7e7e928d25ff038a94fff727f78","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-21T12:31:42Z","title_canon_sha256":"229775981f94451cb2cb6b2810a771a8ee6dd8d9ac5ef919d5a95703275d0b51"},"schema_version":"1.0","source":{"id":"1812.09079","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.09079","created_at":"2026-05-17T23:42:53Z"},{"alias_kind":"arxiv_version","alias_value":"1812.09079v2","created_at":"2026-05-17T23:42:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.09079","created_at":"2026-05-17T23:42:53Z"},{"alias_kind":"pith_short_12","alias_value":"6MGUETQUFLZY","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6MGUETQUFLZY7HLA","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6MGUETQU","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:1b173f49db8087c96c90d01af9585735478987a2fb97173431a2b9663488ed2a","target":"graph","created_at":"2026-05-17T23:42:53Z","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":"In video super-resolution, the spatio-temporal coherence between, and among the frames must be exploited appropriately for accurate prediction of the high resolution frames. Although 2D convolutional neural networks (CNNs) are powerful in modelling images, 3D-CNNs are more suitable for spatio-temporal feature extraction as they can preserve temporal information. To this end, we propose an effective 3D-CNN for video super-resolution, called the 3DSRnet that does not require motion alignment as preprocessing. Our 3DSRnet maintains the temporal depth of spatio-temporal feature maps to maximally c","authors_text":"Jeongyeon Lim, Munchurl Kim, Soo Ye Kim, Taeyoung Na","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-21T12:31:42Z","title":"3DSRnet: Video Super-resolution using 3D Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.09079","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:ad401ecd803f8a9d2e6b70c5f8c8a92d72d98c0cf02add5d15713760b86afa72","target":"record","created_at":"2026-05-17T23:42:53Z","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":"06aa88c5682d8863bc25c9a15fa72efce1b2c7e7e928d25ff038a94fff727f78","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-21T12:31:42Z","title_canon_sha256":"229775981f94451cb2cb6b2810a771a8ee6dd8d9ac5ef919d5a95703275d0b51"},"schema_version":"1.0","source":{"id":"1812.09079","kind":"arxiv","version":2}},"canonical_sha256":"f30d424e142af38f9d607a4d00d98d841a52ab411c8d8639488fca111f28d304","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f30d424e142af38f9d607a4d00d98d841a52ab411c8d8639488fca111f28d304","first_computed_at":"2026-05-17T23:42:53.525978Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:42:53.525978Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"q07+bv7sUf6mjcFbCqFvFnyoBsOayRcRVCi9bCneDzhTHhGESwN1S7MUkzvkALh+kQwighdxzr24DTFhKbQRAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:42:53.526605Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.09079","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ad401ecd803f8a9d2e6b70c5f8c8a92d72d98c0cf02add5d15713760b86afa72","sha256:1b173f49db8087c96c90d01af9585735478987a2fb97173431a2b9663488ed2a"],"state_sha256":"769a17d5f37480fef2f99b26c8c4862e6531488ac2572eb56c2e40fec8f3d7fa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MddujP6x5JFGk6J5ni9oiaqQDhKkVAXlr0NdzShxcsH3XYsHvYK6InkPBZDERi6kz5h87wcWqrPyf9HZDnOkBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T18:08:15.430296Z","bundle_sha256":"23b528cb9fc49583813b56ffd790a83fcec924b6c83871387b3b3ee52520146e"}}