{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:MV3M6DACJ3A5RK6GXI2KJWLKXR","short_pith_number":"pith:MV3M6DAC","schema_version":"1.0","canonical_sha256":"6576cf0c024ec1d8abc6ba34a4d96abc5b261d9133e39daf1142618d74535920","source":{"kind":"arxiv","id":"1707.00471","version":1},"attestation_state":"computed","paper":{"title":"End-to-End Learning of Video Super-Resolution with Motion Compensation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Eddy Ilg, Osama Makansi, Thomas Brox","submitted_at":"2017-07-03T10:16:10Z","abstract_excerpt":"Learning approaches have shown great success in the task of super-resolving an image given a low resolution input. Video super-resolution aims for exploiting additionally the information from multiple images. Typically, the images are related via optical flow and consecutive image warping. In this paper, we provide an end-to-end video super-resolution network that, in contrast to previous works, includes the estimation of optical flow in the overall network architecture. We analyze the usage of optical flow for video super-resolution and find that common off-the-shelf image warping does not al"},"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":"1707.00471","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-03T10:16:10Z","cross_cats_sorted":[],"title_canon_sha256":"af51900373481fd74d9beacf8a3d0a328c40b9c713375ad6e09596126350ce52","abstract_canon_sha256":"33ad44af5d338802b4cd78e4b64f0c47f308b992f91ba99f2b07e03fffed40cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:03.261177Z","signature_b64":"XeBwQW/2OQ3UnT1Jgm4T63/6h/XUT8nhzJJvwHNf7sniTkwqjHcQ3Rrjc2vhybUwvrHJ/GpBpIpvp7LzmeMPDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6576cf0c024ec1d8abc6ba34a4d96abc5b261d9133e39daf1142618d74535920","last_reissued_at":"2026-05-18T00:41:03.260488Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:03.260488Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"End-to-End Learning of Video Super-Resolution with Motion Compensation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Eddy Ilg, Osama Makansi, Thomas Brox","submitted_at":"2017-07-03T10:16:10Z","abstract_excerpt":"Learning approaches have shown great success in the task of super-resolving an image given a low resolution input. Video super-resolution aims for exploiting additionally the information from multiple images. Typically, the images are related via optical flow and consecutive image warping. In this paper, we provide an end-to-end video super-resolution network that, in contrast to previous works, includes the estimation of optical flow in the overall network architecture. We analyze the usage of optical flow for video super-resolution and find that common off-the-shelf image warping does not al"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.00471","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1707.00471","created_at":"2026-05-18T00:41:03.260621+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.00471v1","created_at":"2026-05-18T00:41:03.260621+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.00471","created_at":"2026-05-18T00:41:03.260621+00:00"},{"alias_kind":"pith_short_12","alias_value":"MV3M6DACJ3A5","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_16","alias_value":"MV3M6DACJ3A5RK6G","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_8","alias_value":"MV3M6DAC","created_at":"2026-05-18T12:31:31.346846+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/MV3M6DACJ3A5RK6GXI2KJWLKXR","json":"https://pith.science/pith/MV3M6DACJ3A5RK6GXI2KJWLKXR.json","graph_json":"https://pith.science/api/pith-number/MV3M6DACJ3A5RK6GXI2KJWLKXR/graph.json","events_json":"https://pith.science/api/pith-number/MV3M6DACJ3A5RK6GXI2KJWLKXR/events.json","paper":"https://pith.science/paper/MV3M6DAC"},"agent_actions":{"view_html":"https://pith.science/pith/MV3M6DACJ3A5RK6GXI2KJWLKXR","download_json":"https://pith.science/pith/MV3M6DACJ3A5RK6GXI2KJWLKXR.json","view_paper":"https://pith.science/paper/MV3M6DAC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.00471&json=true","fetch_graph":"https://pith.science/api/pith-number/MV3M6DACJ3A5RK6GXI2KJWLKXR/graph.json","fetch_events":"https://pith.science/api/pith-number/MV3M6DACJ3A5RK6GXI2KJWLKXR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MV3M6DACJ3A5RK6GXI2KJWLKXR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MV3M6DACJ3A5RK6GXI2KJWLKXR/action/storage_attestation","attest_author":"https://pith.science/pith/MV3M6DACJ3A5RK6GXI2KJWLKXR/action/author_attestation","sign_citation":"https://pith.science/pith/MV3M6DACJ3A5RK6GXI2KJWLKXR/action/citation_signature","submit_replication":"https://pith.science/pith/MV3M6DACJ3A5RK6GXI2KJWLKXR/action/replication_record"}},"created_at":"2026-05-18T00:41:03.260621+00:00","updated_at":"2026-05-18T00:41:03.260621+00:00"}