{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:IUXU5D7MEUQMLIK326H5THM7VC","short_pith_number":"pith:IUXU5D7M","schema_version":"1.0","canonical_sha256":"452f4e8fec2520c5a15bd78fd99d9fa8a4d2b700f0d7c1dee943a61dc8f91c0f","source":{"kind":"arxiv","id":"1909.01763","version":1},"attestation_state":"computed","paper":{"title":"Video Affective Effects Prediction with Multi-modal Fusion and Shot-Long Temporal Context","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chaoping Tu, Jie Zhang, Longjun Cai, Wu Wei, Yin Zhao","submitted_at":"2019-09-01T07:22:20Z","abstract_excerpt":"Predicting the emotional impact of videos using machine learning is a challenging task considering the varieties of modalities, the complicated temporal contex of the video as well as the time dependency of the emotional states. Feature extraction, multi-modal fusion and temporal context fusion are crucial stages for predicting valence and arousal values in the emotional impact, but have not been successfully exploited. In this paper, we propose a comprehensive framework with novel designs of modal structure and multi-modal fusion strategy. We select the most suitable modalities for valence an"},"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":"1909.01763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-01T07:22:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2c8db5295e8bca32009d4bbe24fb9b4f295a6e4a2f2b58800b6d92a524ffda3d","abstract_canon_sha256":"f6bd615c30b19f0a4794c83681b37e9da98a2da2a9a15a92058e0a2c74ee4f6b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:02:19.643566Z","signature_b64":"hGdUIyryYZfTQqKo2YrdXCbqVddp4S/5SRWeK9dyR9eY4j2Y0tSSp5OJLdKMYbC5LALkXZnEp7+dgF770nx4DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"452f4e8fec2520c5a15bd78fd99d9fa8a4d2b700f0d7c1dee943a61dc8f91c0f","last_reissued_at":"2026-07-05T00:02:19.643083Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:02:19.643083Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Video Affective Effects Prediction with Multi-modal Fusion and Shot-Long Temporal Context","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chaoping Tu, Jie Zhang, Longjun Cai, Wu Wei, Yin Zhao","submitted_at":"2019-09-01T07:22:20Z","abstract_excerpt":"Predicting the emotional impact of videos using machine learning is a challenging task considering the varieties of modalities, the complicated temporal contex of the video as well as the time dependency of the emotional states. Feature extraction, multi-modal fusion and temporal context fusion are crucial stages for predicting valence and arousal values in the emotional impact, but have not been successfully exploited. In this paper, we propose a comprehensive framework with novel designs of modal structure and multi-modal fusion strategy. We select the most suitable modalities for valence an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.01763","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1909.01763/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":"1909.01763","created_at":"2026-07-05T00:02:19.643141+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.01763v1","created_at":"2026-07-05T00:02:19.643141+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.01763","created_at":"2026-07-05T00:02:19.643141+00:00"},{"alias_kind":"pith_short_12","alias_value":"IUXU5D7MEUQM","created_at":"2026-07-05T00:02:19.643141+00:00"},{"alias_kind":"pith_short_16","alias_value":"IUXU5D7MEUQMLIK3","created_at":"2026-07-05T00:02:19.643141+00:00"},{"alias_kind":"pith_short_8","alias_value":"IUXU5D7M","created_at":"2026-07-05T00:02:19.643141+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/IUXU5D7MEUQMLIK326H5THM7VC","json":"https://pith.science/pith/IUXU5D7MEUQMLIK326H5THM7VC.json","graph_json":"https://pith.science/api/pith-number/IUXU5D7MEUQMLIK326H5THM7VC/graph.json","events_json":"https://pith.science/api/pith-number/IUXU5D7MEUQMLIK326H5THM7VC/events.json","paper":"https://pith.science/paper/IUXU5D7M"},"agent_actions":{"view_html":"https://pith.science/pith/IUXU5D7MEUQMLIK326H5THM7VC","download_json":"https://pith.science/pith/IUXU5D7MEUQMLIK326H5THM7VC.json","view_paper":"https://pith.science/paper/IUXU5D7M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.01763&json=true","fetch_graph":"https://pith.science/api/pith-number/IUXU5D7MEUQMLIK326H5THM7VC/graph.json","fetch_events":"https://pith.science/api/pith-number/IUXU5D7MEUQMLIK326H5THM7VC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IUXU5D7MEUQMLIK326H5THM7VC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IUXU5D7MEUQMLIK326H5THM7VC/action/storage_attestation","attest_author":"https://pith.science/pith/IUXU5D7MEUQMLIK326H5THM7VC/action/author_attestation","sign_citation":"https://pith.science/pith/IUXU5D7MEUQMLIK326H5THM7VC/action/citation_signature","submit_replication":"https://pith.science/pith/IUXU5D7MEUQMLIK326H5THM7VC/action/replication_record"}},"created_at":"2026-07-05T00:02:19.643141+00:00","updated_at":"2026-07-05T00:02:19.643141+00:00"}