{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:AFPQFCGKVOPTVUSEMETFVGCSNX","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ce813ae174d4fe486d255fc836c016560d5dd6e56c4d8e4335c3463c698b49ef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-13T16:57:40Z","title_canon_sha256":"7b5e719b2cc82d0f282ae86118b712c8d4a4b9d60aff5aca30b4203af0330819"},"schema_version":"1.0","source":{"id":"1905.05143","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.05143","created_at":"2026-07-05T00:11:33Z"},{"alias_kind":"arxiv_version","alias_value":"1905.05143v2","created_at":"2026-07-05T00:11:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05143","created_at":"2026-07-05T00:11:33Z"},{"alias_kind":"pith_short_12","alias_value":"AFPQFCGKVOPT","created_at":"2026-07-05T00:11:33Z"},{"alias_kind":"pith_short_16","alias_value":"AFPQFCGKVOPTVUSE","created_at":"2026-07-05T00:11:33Z"},{"alias_kind":"pith_short_8","alias_value":"AFPQFCGK","created_at":"2026-07-05T00:11:33Z"}],"graph_snapshots":[{"event_id":"sha256:c468d55642f5eb97861479b9beec3474d94bee8970096e7d1234ae6e1eed5d9b","target":"graph","created_at":"2026-07-05T00:11:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1905.05143/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many human activities take minutes to unfold. To represent them, related works opt for statistical pooling, which neglects the temporal structure. Others opt for convolutional methods, as CNN and Non-Local. While successful in learning temporal concepts, they are short of modeling minutes-long temporal dependencies. We propose VideoGraph, a method to achieve the best of two worlds: represent minutes-long human activities and learn their underlying temporal structure. VideoGraph learns a graph-based representation for human activities. The graph, its nodes and edges are learned entirely from vi","authors_text":"Arnold W.M. Smeulders, Efstratios Gavves, Noureldien Hussein","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-13T16:57:40Z","title":"VideoGraph: Recognizing Minutes-Long Human Activities in Videos"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05143","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:dea6cde8c1627871c1a7a2ff3d1f65664cf8803c3b63a347d5875c6829220b0d","target":"record","created_at":"2026-07-05T00:11:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ce813ae174d4fe486d255fc836c016560d5dd6e56c4d8e4335c3463c698b49ef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-13T16:57:40Z","title_canon_sha256":"7b5e719b2cc82d0f282ae86118b712c8d4a4b9d60aff5aca30b4203af0330819"},"schema_version":"1.0","source":{"id":"1905.05143","kind":"arxiv","version":2}},"canonical_sha256":"015f0288caab9f3ad24461265a98526dc972b06f5d3646e6d0475e29f4e44f93","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"015f0288caab9f3ad24461265a98526dc972b06f5d3646e6d0475e29f4e44f93","first_computed_at":"2026-07-05T00:11:33.859893Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:11:33.859893Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rjiJQlDoF+wjPwxem2Vk62AZKu4NZV9kmblwf2MMvyfGhBajRsQR/g/fdNauRK3MYebDCUppvGWcrrLt5bUUDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:11:33.860421Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.05143","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dea6cde8c1627871c1a7a2ff3d1f65664cf8803c3b63a347d5875c6829220b0d","sha256:c468d55642f5eb97861479b9beec3474d94bee8970096e7d1234ae6e1eed5d9b"],"state_sha256":"7cdc564cdfbbb01408e61edbcb3e0bb5b650aec99a917a7c7aba44c766a01b07"}