{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:T2TC72EUBO7TU5BT5DMOO7OABE","short_pith_number":"pith:T2TC72EU","canonical_record":{"source":{"id":"1611.07715","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-23T10:06:30Z","cross_cats_sorted":[],"title_canon_sha256":"99dca40d8fb02bbb064ad8c09deff3eceb7873db401ec8692aeb2385a225d932","abstract_canon_sha256":"66716eea572298f51ccd5c4c12fcb2f9ab8c0e0ef845c5e99f6dc271d83dfb0b"},"schema_version":"1.0"},"canonical_sha256":"9ea62fe8940bbf3a7433e8d8e77dc0093cf70c625fdf3702bd707baef9738d94","source":{"kind":"arxiv","id":"1611.07715","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.07715","created_at":"2026-05-18T00:43:09Z"},{"alias_kind":"arxiv_version","alias_value":"1611.07715v2","created_at":"2026-05-18T00:43:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.07715","created_at":"2026-05-18T00:43:09Z"},{"alias_kind":"pith_short_12","alias_value":"T2TC72EUBO7T","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_16","alias_value":"T2TC72EUBO7TU5BT","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_8","alias_value":"T2TC72EU","created_at":"2026-05-18T12:30:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:T2TC72EUBO7TU5BT5DMOO7OABE","target":"record","payload":{"canonical_record":{"source":{"id":"1611.07715","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-23T10:06:30Z","cross_cats_sorted":[],"title_canon_sha256":"99dca40d8fb02bbb064ad8c09deff3eceb7873db401ec8692aeb2385a225d932","abstract_canon_sha256":"66716eea572298f51ccd5c4c12fcb2f9ab8c0e0ef845c5e99f6dc271d83dfb0b"},"schema_version":"1.0"},"canonical_sha256":"9ea62fe8940bbf3a7433e8d8e77dc0093cf70c625fdf3702bd707baef9738d94","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:09.016603Z","signature_b64":"/uOBTEMYgUTUwK8dSfeTvnWaGH3CugBLCko7yhu5ad2iiaNXs4bL3mJ6SSY6d387oubmc8pDmI1TMx3+eUEiBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ea62fe8940bbf3a7433e8d8e77dc0093cf70c625fdf3702bd707baef9738d94","last_reissued_at":"2026-05-18T00:43:09.015849Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:09.015849Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.07715","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":"2026-05-18T00:43:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NoB5IU9lDprU/AbcISc+S6MaWKQnRg9NWxx+3t+NzIoQdCkDzLCYkg8jGllHrYlyZA37AH1LF3xcCCrIr83mDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:30:48.538525Z"},"content_sha256":"a0329df5acc058838f7b1f0ab12394c2ae1350da51fce2c2fd6bf7d0a94128c8","schema_version":"1.0","event_id":"sha256:a0329df5acc058838f7b1f0ab12394c2ae1350da51fce2c2fd6bf7d0a94128c8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:T2TC72EUBO7TU5BT5DMOO7OABE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Feature Flow for Video Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jifeng Dai, Lu Yuan, Xizhou Zhu, Yichen Wei, Yuwen Xiong","submitted_at":"2016-11-23T10:06:30Z","abstract_excerpt":"Deep convolutional neutral networks have achieved great success on image recognition tasks. Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable. We present deep feature flow, a fast and accurate framework for video recognition. It runs the expensive convolutional sub-network only on sparse key frames and propagates their deep feature maps to other frames via a flow field. It achieves significant speedup as flow computation is relatively fast. The end-to-end training of the whole architecture significa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.07715","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":"2026-05-18T00:43:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o+kow7chQVX3WbyhqhhjQ8pMOeRKIUuVVHgUYA/mtMS8XWsadMKlHvXuqMEA8/ig39pwuqcAIrFIXl+lVP62CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:30:48.539189Z"},"content_sha256":"0d27eb5d005b6d61a2f796e33d167507d1b58875c63884dbfe1c303dc415e402","schema_version":"1.0","event_id":"sha256:0d27eb5d005b6d61a2f796e33d167507d1b58875c63884dbfe1c303dc415e402"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T2TC72EUBO7TU5BT5DMOO7OABE/bundle.json","state_url":"https://pith.science/pith/T2TC72EUBO7TU5BT5DMOO7OABE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T2TC72EUBO7TU5BT5DMOO7OABE/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":"2026-05-26T16:30:48Z","links":{"resolver":"https://pith.science/pith/T2TC72EUBO7TU5BT5DMOO7OABE","bundle":"https://pith.science/pith/T2TC72EUBO7TU5BT5DMOO7OABE/bundle.json","state":"https://pith.science/pith/T2TC72EUBO7TU5BT5DMOO7OABE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T2TC72EUBO7TU5BT5DMOO7OABE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:T2TC72EUBO7TU5BT5DMOO7OABE","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":"66716eea572298f51ccd5c4c12fcb2f9ab8c0e0ef845c5e99f6dc271d83dfb0b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-23T10:06:30Z","title_canon_sha256":"99dca40d8fb02bbb064ad8c09deff3eceb7873db401ec8692aeb2385a225d932"},"schema_version":"1.0","source":{"id":"1611.07715","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.07715","created_at":"2026-05-18T00:43:09Z"},{"alias_kind":"arxiv_version","alias_value":"1611.07715v2","created_at":"2026-05-18T00:43:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.07715","created_at":"2026-05-18T00:43:09Z"},{"alias_kind":"pith_short_12","alias_value":"T2TC72EUBO7T","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_16","alias_value":"T2TC72EUBO7TU5BT","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_8","alias_value":"T2TC72EU","created_at":"2026-05-18T12:30:44Z"}],"graph_snapshots":[{"event_id":"sha256:0d27eb5d005b6d61a2f796e33d167507d1b58875c63884dbfe1c303dc415e402","target":"graph","created_at":"2026-05-18T00:43:09Z","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":"Deep convolutional neutral networks have achieved great success on image recognition tasks. Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable. We present deep feature flow, a fast and accurate framework for video recognition. It runs the expensive convolutional sub-network only on sparse key frames and propagates their deep feature maps to other frames via a flow field. It achieves significant speedup as flow computation is relatively fast. The end-to-end training of the whole architecture significa","authors_text":"Jifeng Dai, Lu Yuan, Xizhou Zhu, Yichen Wei, Yuwen Xiong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-23T10:06:30Z","title":"Deep Feature Flow for Video Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.07715","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:a0329df5acc058838f7b1f0ab12394c2ae1350da51fce2c2fd6bf7d0a94128c8","target":"record","created_at":"2026-05-18T00:43:09Z","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":"66716eea572298f51ccd5c4c12fcb2f9ab8c0e0ef845c5e99f6dc271d83dfb0b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-23T10:06:30Z","title_canon_sha256":"99dca40d8fb02bbb064ad8c09deff3eceb7873db401ec8692aeb2385a225d932"},"schema_version":"1.0","source":{"id":"1611.07715","kind":"arxiv","version":2}},"canonical_sha256":"9ea62fe8940bbf3a7433e8d8e77dc0093cf70c625fdf3702bd707baef9738d94","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9ea62fe8940bbf3a7433e8d8e77dc0093cf70c625fdf3702bd707baef9738d94","first_computed_at":"2026-05-18T00:43:09.015849Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:43:09.015849Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/uOBTEMYgUTUwK8dSfeTvnWaGH3CugBLCko7yhu5ad2iiaNXs4bL3mJ6SSY6d387oubmc8pDmI1TMx3+eUEiBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:43:09.016603Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.07715","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a0329df5acc058838f7b1f0ab12394c2ae1350da51fce2c2fd6bf7d0a94128c8","sha256:0d27eb5d005b6d61a2f796e33d167507d1b58875c63884dbfe1c303dc415e402"],"state_sha256":"e9aa0110cca749cd989995bd7b3d7f33947e0c86c54d4116468c159cc1c16d81"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8BOPIzp9S8s/OOwNuyWx2Ys2s9XRrh1XvmsQdS3QnoR8ylCi0syhmHr4XyaVKM3i0ymDsnLPVIvVczElXEiGDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T16:30:48.542632Z","bundle_sha256":"3cc1476841038c3c77552ed5a5a8048228a07a23afb2cacb7211441c95212dc8"}}