{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:XMFBBXTULCEXLX5W5J22SIN3MJ","short_pith_number":"pith:XMFBBXTU","canonical_record":{"source":{"id":"1511.02917","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-09T22:30:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a6da17fc3186ae330eef732e902cef0dc0b20d06f72024a34a2f886670158771","abstract_canon_sha256":"b5ae0124e14d2936207fbe12bfed5e110cd2264f0d55ccc474013d319b45e86d"},"schema_version":"1.0"},"canonical_sha256":"bb0a10de74588975dfb6ea75a921bb6267a548f19cca259573835e8ce415f474","source":{"kind":"arxiv","id":"1511.02917","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.02917","created_at":"2026-05-18T01:18:57Z"},{"alias_kind":"arxiv_version","alias_value":"1511.02917v2","created_at":"2026-05-18T01:18:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.02917","created_at":"2026-05-18T01:18:57Z"},{"alias_kind":"pith_short_12","alias_value":"XMFBBXTULCEX","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_16","alias_value":"XMFBBXTULCEXLX5W","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_8","alias_value":"XMFBBXTU","created_at":"2026-05-18T12:29:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:XMFBBXTULCEXLX5W5J22SIN3MJ","target":"record","payload":{"canonical_record":{"source":{"id":"1511.02917","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-09T22:30:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a6da17fc3186ae330eef732e902cef0dc0b20d06f72024a34a2f886670158771","abstract_canon_sha256":"b5ae0124e14d2936207fbe12bfed5e110cd2264f0d55ccc474013d319b45e86d"},"schema_version":"1.0"},"canonical_sha256":"bb0a10de74588975dfb6ea75a921bb6267a548f19cca259573835e8ce415f474","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:18:57.120136Z","signature_b64":"5cgYf7+7UMOKvENnjAz02k9TiUyF4m46uXXOxFckICp2foROvAsKliJnkMdAnm1wgeCXw73huGy3ydM3cMh0Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb0a10de74588975dfb6ea75a921bb6267a548f19cca259573835e8ce415f474","last_reissued_at":"2026-05-18T01:18:57.119752Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:18:57.119752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.02917","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:18:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cyZ4d4mfRDaWQtbjVGlfefYaOlgPKrq7Iju21yCCBwGdmL5itQRsqWqQ/sKtT6p9WdvphTMNsOWEnSPntZqyBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T15:38:00.987863Z"},"content_sha256":"3ee2a2a15969bb91615a146dfe2a54c4560dc5e59bd93a258db6f8979a9c4c4e","schema_version":"1.0","event_id":"sha256:3ee2a2a15969bb91615a146dfe2a54c4560dc5e59bd93a258db6f8979a9c4c4e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:XMFBBXTULCEXLX5W5J22SIN3MJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Detecting events and key actors in multi-person videos","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Alexander Gorban, Jonathan Huang, Kevin Murphy, Li Fei-Fei, Sami Abu-el-haija, Vignesh Ramanathan","submitted_at":"2015-11-09T22:30:19Z","abstract_excerpt":"Multi-person event recognition is a challenging task, often with many people active in the scene but only a small subset contributing to an actual event. In this paper, we propose a model which learns to detect events in such videos while automatically \"attending\" to the people responsible for the event. Our model does not use explicit annotations regarding who or where those people are during training and testing. In particular, we track people in videos and use a recurrent neural network (RNN) to represent the track features. We learn time-varying attention weights to combine these features "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.02917","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:18:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yfA5yFP2gkT2CF2+SDl5+k1p+QEVKHrC90kNQGeccl3SPlAA3oy2psSrPYURrdVWYE+2VFHouSPJpPooYN+VAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T15:38:00.988469Z"},"content_sha256":"ad038fae6e5c9c0d732abb3de14399a5202e0ae22a6a16da94f39ff6b6ab8a34","schema_version":"1.0","event_id":"sha256:ad038fae6e5c9c0d732abb3de14399a5202e0ae22a6a16da94f39ff6b6ab8a34"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XMFBBXTULCEXLX5W5J22SIN3MJ/bundle.json","state_url":"https://pith.science/pith/XMFBBXTULCEXLX5W5J22SIN3MJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XMFBBXTULCEXLX5W5J22SIN3MJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-07T15:38:00Z","links":{"resolver":"https://pith.science/pith/XMFBBXTULCEXLX5W5J22SIN3MJ","bundle":"https://pith.science/pith/XMFBBXTULCEXLX5W5J22SIN3MJ/bundle.json","state":"https://pith.science/pith/XMFBBXTULCEXLX5W5J22SIN3MJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XMFBBXTULCEXLX5W5J22SIN3MJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:XMFBBXTULCEXLX5W5J22SIN3MJ","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":"b5ae0124e14d2936207fbe12bfed5e110cd2264f0d55ccc474013d319b45e86d","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-09T22:30:19Z","title_canon_sha256":"a6da17fc3186ae330eef732e902cef0dc0b20d06f72024a34a2f886670158771"},"schema_version":"1.0","source":{"id":"1511.02917","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.02917","created_at":"2026-05-18T01:18:57Z"},{"alias_kind":"arxiv_version","alias_value":"1511.02917v2","created_at":"2026-05-18T01:18:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.02917","created_at":"2026-05-18T01:18:57Z"},{"alias_kind":"pith_short_12","alias_value":"XMFBBXTULCEX","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_16","alias_value":"XMFBBXTULCEXLX5W","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_8","alias_value":"XMFBBXTU","created_at":"2026-05-18T12:29:50Z"}],"graph_snapshots":[{"event_id":"sha256:ad038fae6e5c9c0d732abb3de14399a5202e0ae22a6a16da94f39ff6b6ab8a34","target":"graph","created_at":"2026-05-18T01:18:57Z","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"},"paper":{"abstract_excerpt":"Multi-person event recognition is a challenging task, often with many people active in the scene but only a small subset contributing to an actual event. In this paper, we propose a model which learns to detect events in such videos while automatically \"attending\" to the people responsible for the event. Our model does not use explicit annotations regarding who or where those people are during training and testing. In particular, we track people in videos and use a recurrent neural network (RNN) to represent the track features. We learn time-varying attention weights to combine these features ","authors_text":"Alexander Gorban, Jonathan Huang, Kevin Murphy, Li Fei-Fei, Sami Abu-el-haija, Vignesh Ramanathan","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-09T22:30:19Z","title":"Detecting events and key actors in multi-person videos"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.02917","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":"sha256:3ee2a2a15969bb91615a146dfe2a54c4560dc5e59bd93a258db6f8979a9c4c4e","target":"record","created_at":"2026-05-18T01:18:57Z","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":"b5ae0124e14d2936207fbe12bfed5e110cd2264f0d55ccc474013d319b45e86d","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-09T22:30:19Z","title_canon_sha256":"a6da17fc3186ae330eef732e902cef0dc0b20d06f72024a34a2f886670158771"},"schema_version":"1.0","source":{"id":"1511.02917","kind":"arxiv","version":2}},"canonical_sha256":"bb0a10de74588975dfb6ea75a921bb6267a548f19cca259573835e8ce415f474","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bb0a10de74588975dfb6ea75a921bb6267a548f19cca259573835e8ce415f474","first_computed_at":"2026-05-18T01:18:57.119752Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:18:57.119752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5cgYf7+7UMOKvENnjAz02k9TiUyF4m46uXXOxFckICp2foROvAsKliJnkMdAnm1wgeCXw73huGy3ydM3cMh0Bg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:18:57.120136Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.02917","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3ee2a2a15969bb91615a146dfe2a54c4560dc5e59bd93a258db6f8979a9c4c4e","sha256:ad038fae6e5c9c0d732abb3de14399a5202e0ae22a6a16da94f39ff6b6ab8a34"],"state_sha256":"73f681488934a7e085ee3da45c64050c905ffb134187b44561512fb8f05994d7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p0QCEuDeEGQ8VPBD8FOx3LycBbJ0NQj+q9cmSm0v913r9qLdcQnpSBqhO20erLy/NHFIh1nOnX5c5mKIlVs6Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T15:38:00.991697Z","bundle_sha256":"3c69bef2257847113a032799f33e8b47add1a6a565e6c962c335d030f226867b"}}