{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EJ5D34RCSNPF5OK3RUCGFKDIWA","short_pith_number":"pith:EJ5D34RC","schema_version":"1.0","canonical_sha256":"227a3df222935e5eb95b8d0462a868b01d4e50fb56de45a586054971d90083ec","source":{"kind":"arxiv","id":"2406.09754","version":2},"attestation_state":"computed","paper":{"title":"LAVIB: A Large-scale Video Interpolation Benchmark","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexandros Stergiou","submitted_at":"2024-06-14T06:44:01Z","abstract_excerpt":"This paper introduces a LArge-scale Video Interpolation Benchmark (LAVIB) for the low-level video task of Video Frame Interpolation (VFI). LAVIB comprises a large collection of high-resolution videos sourced from the web through an automated pipeline with minimal requirements for human verification. Metrics are computed for each video's motion magnitudes, luminance conditions, frame sharpness, and contrast. The collection of videos and the creation of quantitative challenges based on these metrics are under-explored by current low-level video task datasets. In total, LAVIB includes 283K clips "},"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":"2406.09754","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-14T06:44:01Z","cross_cats_sorted":[],"title_canon_sha256":"c9bec91c4fd8a7bcb7f7628fb6a7a65aded6d6e02c9889d08e2cfb63cd558067","abstract_canon_sha256":"c768706e7e52ea9c2037790b39b08eba2079d6092bfe49c999a9fceff0168b16"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:23:07.096042Z","signature_b64":"kUZVRXfl5aiQ9oQgOc5eiXLBZbHkFsDWylkzkeEm+2aDL+qJMZqdwg/kGcwsQBjgUbPlJIHKEAGgOkNVZcQHAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"227a3df222935e5eb95b8d0462a868b01d4e50fb56de45a586054971d90083ec","last_reissued_at":"2026-07-05T09:23:07.095405Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:23:07.095405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LAVIB: A Large-scale Video Interpolation Benchmark","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexandros Stergiou","submitted_at":"2024-06-14T06:44:01Z","abstract_excerpt":"This paper introduces a LArge-scale Video Interpolation Benchmark (LAVIB) for the low-level video task of Video Frame Interpolation (VFI). LAVIB comprises a large collection of high-resolution videos sourced from the web through an automated pipeline with minimal requirements for human verification. Metrics are computed for each video's motion magnitudes, luminance conditions, frame sharpness, and contrast. The collection of videos and the creation of quantitative challenges based on these metrics are under-explored by current low-level video task datasets. In total, LAVIB includes 283K clips "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.09754","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/2406.09754/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2406.09754","created_at":"2026-07-05T09:23:07.095500+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.09754v2","created_at":"2026-07-05T09:23:07.095500+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.09754","created_at":"2026-07-05T09:23:07.095500+00:00"},{"alias_kind":"pith_short_12","alias_value":"EJ5D34RCSNPF","created_at":"2026-07-05T09:23:07.095500+00:00"},{"alias_kind":"pith_short_16","alias_value":"EJ5D34RCSNPF5OK3","created_at":"2026-07-05T09:23:07.095500+00:00"},{"alias_kind":"pith_short_8","alias_value":"EJ5D34RC","created_at":"2026-07-05T09:23:07.095500+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.03578","citing_title":"Diffusing in the Right Space: A Systematic Study of Latent Diffusability","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2606.29166","citing_title":"A Self-Supervised Learning Framework for Video Encoding Complexity Clustering","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EJ5D34RCSNPF5OK3RUCGFKDIWA","json":"https://pith.science/pith/EJ5D34RCSNPF5OK3RUCGFKDIWA.json","graph_json":"https://pith.science/api/pith-number/EJ5D34RCSNPF5OK3RUCGFKDIWA/graph.json","events_json":"https://pith.science/api/pith-number/EJ5D34RCSNPF5OK3RUCGFKDIWA/events.json","paper":"https://pith.science/paper/EJ5D34RC"},"agent_actions":{"view_html":"https://pith.science/pith/EJ5D34RCSNPF5OK3RUCGFKDIWA","download_json":"https://pith.science/pith/EJ5D34RCSNPF5OK3RUCGFKDIWA.json","view_paper":"https://pith.science/paper/EJ5D34RC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.09754&json=true","fetch_graph":"https://pith.science/api/pith-number/EJ5D34RCSNPF5OK3RUCGFKDIWA/graph.json","fetch_events":"https://pith.science/api/pith-number/EJ5D34RCSNPF5OK3RUCGFKDIWA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EJ5D34RCSNPF5OK3RUCGFKDIWA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EJ5D34RCSNPF5OK3RUCGFKDIWA/action/storage_attestation","attest_author":"https://pith.science/pith/EJ5D34RCSNPF5OK3RUCGFKDIWA/action/author_attestation","sign_citation":"https://pith.science/pith/EJ5D34RCSNPF5OK3RUCGFKDIWA/action/citation_signature","submit_replication":"https://pith.science/pith/EJ5D34RCSNPF5OK3RUCGFKDIWA/action/replication_record"}},"created_at":"2026-07-05T09:23:07.095500+00:00","updated_at":"2026-07-05T09:23:07.095500+00:00"}