{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HD7ECGGILWAQQ5627IZ7XMQT2G","short_pith_number":"pith:HD7ECGGI","schema_version":"1.0","canonical_sha256":"38fe4118c85d810877dafa33fbb213d195d077591fa14cdcbed3dfdb9d7ffa8c","source":{"kind":"arxiv","id":"2404.18156","version":1},"attestation_state":"computed","paper":{"title":"Event-based Video Frame Interpolation with Edge Guided Motion Refinement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bochen Xie, Hao Chen, Yongjian Deng, Youfu Li, Yuhan Liu, Zhen Yang","submitted_at":"2024-04-28T12:13:34Z","abstract_excerpt":"Video frame interpolation, the process of synthesizing intermediate frames between sequential video frames, has made remarkable progress with the use of event cameras. These sensors, with microsecond-level temporal resolution, fill information gaps between frames by providing precise motion cues. However, contemporary Event-Based Video Frame Interpolation (E-VFI) techniques often neglect the fact that event data primarily supply high-confidence features at scene edges during multi-modal feature fusion, thereby diminishing the role of event signals in optical flow (OF) estimation and warping re"},"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":"2404.18156","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-04-28T12:13:34Z","cross_cats_sorted":[],"title_canon_sha256":"924ce339cccf9f2b32beb38feed5bb312f8cf9a01b00b74fbd4ffd330ba87875","abstract_canon_sha256":"38c2ea6079b3d06b2f47208b0d074cdd454f6f9350a5d2237064079c2634cfee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:12:59.088395Z","signature_b64":"XzlCCoBZ18VTmnwlGRJioHahjs6C+BjBpB2uJR5Roogmvr4OfdifYARqhSoFjkbUza1Dh5+jR39E+n4idN4wAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38fe4118c85d810877dafa33fbb213d195d077591fa14cdcbed3dfdb9d7ffa8c","last_reissued_at":"2026-07-05T08:12:59.088026Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:12:59.088026Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Event-based Video Frame Interpolation with Edge Guided Motion Refinement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bochen Xie, Hao Chen, Yongjian Deng, Youfu Li, Yuhan Liu, Zhen Yang","submitted_at":"2024-04-28T12:13:34Z","abstract_excerpt":"Video frame interpolation, the process of synthesizing intermediate frames between sequential video frames, has made remarkable progress with the use of event cameras. These sensors, with microsecond-level temporal resolution, fill information gaps between frames by providing precise motion cues. However, contemporary Event-Based Video Frame Interpolation (E-VFI) techniques often neglect the fact that event data primarily supply high-confidence features at scene edges during multi-modal feature fusion, thereby diminishing the role of event signals in optical flow (OF) estimation and warping re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.18156","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/2404.18156/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":"2404.18156","created_at":"2026-07-05T08:12:59.088090+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.18156v1","created_at":"2026-07-05T08:12:59.088090+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.18156","created_at":"2026-07-05T08:12:59.088090+00:00"},{"alias_kind":"pith_short_12","alias_value":"HD7ECGGILWAQ","created_at":"2026-07-05T08:12:59.088090+00:00"},{"alias_kind":"pith_short_16","alias_value":"HD7ECGGILWAQQ562","created_at":"2026-07-05T08:12:59.088090+00:00"},{"alias_kind":"pith_short_8","alias_value":"HD7ECGGI","created_at":"2026-07-05T08:12:59.088090+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08770","citing_title":"LongE2V: Long-Horizon Event-based Video Reconstruction, Prediction, and Frame Interpolation with Video Diffusion Models","ref_index":63,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HD7ECGGILWAQQ5627IZ7XMQT2G","json":"https://pith.science/pith/HD7ECGGILWAQQ5627IZ7XMQT2G.json","graph_json":"https://pith.science/api/pith-number/HD7ECGGILWAQQ5627IZ7XMQT2G/graph.json","events_json":"https://pith.science/api/pith-number/HD7ECGGILWAQQ5627IZ7XMQT2G/events.json","paper":"https://pith.science/paper/HD7ECGGI"},"agent_actions":{"view_html":"https://pith.science/pith/HD7ECGGILWAQQ5627IZ7XMQT2G","download_json":"https://pith.science/pith/HD7ECGGILWAQQ5627IZ7XMQT2G.json","view_paper":"https://pith.science/paper/HD7ECGGI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.18156&json=true","fetch_graph":"https://pith.science/api/pith-number/HD7ECGGILWAQQ5627IZ7XMQT2G/graph.json","fetch_events":"https://pith.science/api/pith-number/HD7ECGGILWAQQ5627IZ7XMQT2G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HD7ECGGILWAQQ5627IZ7XMQT2G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HD7ECGGILWAQQ5627IZ7XMQT2G/action/storage_attestation","attest_author":"https://pith.science/pith/HD7ECGGILWAQQ5627IZ7XMQT2G/action/author_attestation","sign_citation":"https://pith.science/pith/HD7ECGGILWAQQ5627IZ7XMQT2G/action/citation_signature","submit_replication":"https://pith.science/pith/HD7ECGGILWAQQ5627IZ7XMQT2G/action/replication_record"}},"created_at":"2026-07-05T08:12:59.088090+00:00","updated_at":"2026-07-05T08:12:59.088090+00:00"}