{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:UNSSEWBFNN4A4JPLFXJDKTKHED","short_pith_number":"pith:UNSSEWBF","schema_version":"1.0","canonical_sha256":"a3652258256b780e25eb2dd2354d4720e31b0d8db698c0927deec20babe3a91a","source":{"kind":"arxiv","id":"2109.05569","version":3},"attestation_state":"computed","paper":{"title":"MovieCuts: A New Dataset and Benchmark for Cut Type Recognition","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alejandro Pardo, Ali Thabet, Bernard Ghanem, Fabian Caba Heilbron, Juan Le\\'on Alc\\'azar","submitted_at":"2021-09-12T17:36:55Z","abstract_excerpt":"Understanding movies and their structural patterns is a crucial task in decoding the craft of video editing. While previous works have developed tools for general analysis, such as detecting characters or recognizing cinematography properties at the shot level, less effort has been devoted to understanding the most basic video edit, the Cut. This paper introduces the Cut type recognition task, which requires modeling multi-modal information. To ignite research in this new task, we construct a large-scale dataset called MovieCuts, which contains 173,967 video clips labeled with ten cut types de"},"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":"2109.05569","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-12T17:36:55Z","cross_cats_sorted":[],"title_canon_sha256":"a7c8ff2cd9d52accc87bdb7f2bfb5b82446c2bad883931c829df1d4cd23cab4c","abstract_canon_sha256":"df333e3eda0c87b720030d47edc4c3321e8d2d722bbc318e6e6cad61a158b51f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:12.888366Z","signature_b64":"4iDKyTNOQbC8I00WeXLTRvSP3eQkweJqmcH4zFL7+YFqzVs9AMmd66a34ycAL//4AjHVEkjrL1TXP8JrPkeIAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3652258256b780e25eb2dd2354d4720e31b0d8db698c0927deec20babe3a91a","last_reissued_at":"2026-07-05T05:09:12.887946Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:12.887946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MovieCuts: A New Dataset and Benchmark for Cut Type Recognition","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alejandro Pardo, Ali Thabet, Bernard Ghanem, Fabian Caba Heilbron, Juan Le\\'on Alc\\'azar","submitted_at":"2021-09-12T17:36:55Z","abstract_excerpt":"Understanding movies and their structural patterns is a crucial task in decoding the craft of video editing. While previous works have developed tools for general analysis, such as detecting characters or recognizing cinematography properties at the shot level, less effort has been devoted to understanding the most basic video edit, the Cut. This paper introduces the Cut type recognition task, which requires modeling multi-modal information. To ignite research in this new task, we construct a large-scale dataset called MovieCuts, which contains 173,967 video clips labeled with ten cut types de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.05569","kind":"arxiv","version":3},"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/2109.05569/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":"2109.05569","created_at":"2026-07-05T05:09:12.888005+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.05569v3","created_at":"2026-07-05T05:09:12.888005+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.05569","created_at":"2026-07-05T05:09:12.888005+00:00"},{"alias_kind":"pith_short_12","alias_value":"UNSSEWBFNN4A","created_at":"2026-07-05T05:09:12.888005+00:00"},{"alias_kind":"pith_short_16","alias_value":"UNSSEWBFNN4A4JPL","created_at":"2026-07-05T05:09:12.888005+00:00"},{"alias_kind":"pith_short_8","alias_value":"UNSSEWBF","created_at":"2026-07-05T05:09:12.888005+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/UNSSEWBFNN4A4JPLFXJDKTKHED","json":"https://pith.science/pith/UNSSEWBFNN4A4JPLFXJDKTKHED.json","graph_json":"https://pith.science/api/pith-number/UNSSEWBFNN4A4JPLFXJDKTKHED/graph.json","events_json":"https://pith.science/api/pith-number/UNSSEWBFNN4A4JPLFXJDKTKHED/events.json","paper":"https://pith.science/paper/UNSSEWBF"},"agent_actions":{"view_html":"https://pith.science/pith/UNSSEWBFNN4A4JPLFXJDKTKHED","download_json":"https://pith.science/pith/UNSSEWBFNN4A4JPLFXJDKTKHED.json","view_paper":"https://pith.science/paper/UNSSEWBF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.05569&json=true","fetch_graph":"https://pith.science/api/pith-number/UNSSEWBFNN4A4JPLFXJDKTKHED/graph.json","fetch_events":"https://pith.science/api/pith-number/UNSSEWBFNN4A4JPLFXJDKTKHED/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UNSSEWBFNN4A4JPLFXJDKTKHED/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UNSSEWBFNN4A4JPLFXJDKTKHED/action/storage_attestation","attest_author":"https://pith.science/pith/UNSSEWBFNN4A4JPLFXJDKTKHED/action/author_attestation","sign_citation":"https://pith.science/pith/UNSSEWBFNN4A4JPLFXJDKTKHED/action/citation_signature","submit_replication":"https://pith.science/pith/UNSSEWBFNN4A4JPLFXJDKTKHED/action/replication_record"}},"created_at":"2026-07-05T05:09:12.888005+00:00","updated_at":"2026-07-05T05:09:12.888005+00:00"}