{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:UQSG6HTZDBJPTRGOEP3SOMHVNF","short_pith_number":"pith:UQSG6HTZ","canonical_record":{"source":{"id":"1608.07138","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-25T13:37:15Z","cross_cats_sorted":[],"title_canon_sha256":"e4ef75357984b5e64bdb2f3ac23e6746fe2ed6886e8f8f49b7264eb05e6f63ac","abstract_canon_sha256":"2b5df96309db32a8e94bf774992c922e3c3ca8dc5b4a068608ea5628177340a7"},"schema_version":"1.0"},"canonical_sha256":"a4246f1e791852f9c4ce23f72730f5695904f86c78a31f77b7ca10e4dfc59786","source":{"kind":"arxiv","id":"1608.07138","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.07138","created_at":"2026-05-18T01:07:54Z"},{"alias_kind":"arxiv_version","alias_value":"1608.07138v1","created_at":"2026-05-18T01:07:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.07138","created_at":"2026-05-18T01:07:54Z"},{"alias_kind":"pith_short_12","alias_value":"UQSG6HTZDBJP","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"UQSG6HTZDBJPTRGO","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"UQSG6HTZ","created_at":"2026-05-18T12:30:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:UQSG6HTZDBJPTRGOEP3SOMHVNF","target":"record","payload":{"canonical_record":{"source":{"id":"1608.07138","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-25T13:37:15Z","cross_cats_sorted":[],"title_canon_sha256":"e4ef75357984b5e64bdb2f3ac23e6746fe2ed6886e8f8f49b7264eb05e6f63ac","abstract_canon_sha256":"2b5df96309db32a8e94bf774992c922e3c3ca8dc5b4a068608ea5628177340a7"},"schema_version":"1.0"},"canonical_sha256":"a4246f1e791852f9c4ce23f72730f5695904f86c78a31f77b7ca10e4dfc59786","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:07:54.729743Z","signature_b64":"7Grd61PnDdh3rMoWgAia0K4aiiq6J9UZX50EH1sn0iE1xkd3BOO1FR3oA3vXVSW1xnDvZjGzyh0RFSm26l/HDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4246f1e791852f9c4ce23f72730f5695904f86c78a31f77b7ca10e4dfc59786","last_reissued_at":"2026-05-18T01:07:54.729283Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:07:54.729283Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.07138","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:07:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"un/zFspTng9vWrIqISESBDG3l6PMDa8umvDBGMa5GsKlNPFWXbxZEV65iJo1gAtziYS4JqvL3MKsvZ7BpyfWCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T19:50:24.133800Z"},"content_sha256":"ac06332ea951b25f2f341369cefc6256ef77ee2ea9fed85391e6847e7609052f","schema_version":"1.0","event_id":"sha256:ac06332ea951b25f2f341369cefc6256ef77ee2ea9fed85391e6847e7609052f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:UQSG6HTZDBJPTRGOEP3SOMHVNF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adrien Gaidon, Antonio Manuel L\\'opez, C\\'esar Roberto de Souza, Eleonora Vig","submitted_at":"2016-08-25T13:37:15Z","abstract_excerpt":"Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted spatio-temporal features classified by supe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.07138","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":""},"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":"2026-05-18T01:07:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kyKwcfUggDhs205zAt4QN/7+89aGVskHttHvo6DESJRo/JALO02NTwCd2MaqbxdREmH+41JBvSoazYuRDvWLCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T19:50:24.134600Z"},"content_sha256":"d7007f3a29837ba053be088c4b7752cd9d3cf9d0d0288aedcd00ddd4a9772abd","schema_version":"1.0","event_id":"sha256:d7007f3a29837ba053be088c4b7752cd9d3cf9d0d0288aedcd00ddd4a9772abd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UQSG6HTZDBJPTRGOEP3SOMHVNF/bundle.json","state_url":"https://pith.science/pith/UQSG6HTZDBJPTRGOEP3SOMHVNF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UQSG6HTZDBJPTRGOEP3SOMHVNF/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":"2026-05-31T19:50:24Z","links":{"resolver":"https://pith.science/pith/UQSG6HTZDBJPTRGOEP3SOMHVNF","bundle":"https://pith.science/pith/UQSG6HTZDBJPTRGOEP3SOMHVNF/bundle.json","state":"https://pith.science/pith/UQSG6HTZDBJPTRGOEP3SOMHVNF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UQSG6HTZDBJPTRGOEP3SOMHVNF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:UQSG6HTZDBJPTRGOEP3SOMHVNF","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":"2b5df96309db32a8e94bf774992c922e3c3ca8dc5b4a068608ea5628177340a7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-25T13:37:15Z","title_canon_sha256":"e4ef75357984b5e64bdb2f3ac23e6746fe2ed6886e8f8f49b7264eb05e6f63ac"},"schema_version":"1.0","source":{"id":"1608.07138","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.07138","created_at":"2026-05-18T01:07:54Z"},{"alias_kind":"arxiv_version","alias_value":"1608.07138v1","created_at":"2026-05-18T01:07:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.07138","created_at":"2026-05-18T01:07:54Z"},{"alias_kind":"pith_short_12","alias_value":"UQSG6HTZDBJP","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"UQSG6HTZDBJPTRGO","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"UQSG6HTZ","created_at":"2026-05-18T12:30:46Z"}],"graph_snapshots":[{"event_id":"sha256:d7007f3a29837ba053be088c4b7752cd9d3cf9d0d0288aedcd00ddd4a9772abd","target":"graph","created_at":"2026-05-18T01:07:54Z","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":"Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted spatio-temporal features classified by supe","authors_text":"Adrien Gaidon, Antonio Manuel L\\'opez, C\\'esar Roberto de Souza, Eleonora Vig","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-25T13:37:15Z","title":"Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.07138","kind":"arxiv","version":1},"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:ac06332ea951b25f2f341369cefc6256ef77ee2ea9fed85391e6847e7609052f","target":"record","created_at":"2026-05-18T01:07:54Z","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":"2b5df96309db32a8e94bf774992c922e3c3ca8dc5b4a068608ea5628177340a7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-25T13:37:15Z","title_canon_sha256":"e4ef75357984b5e64bdb2f3ac23e6746fe2ed6886e8f8f49b7264eb05e6f63ac"},"schema_version":"1.0","source":{"id":"1608.07138","kind":"arxiv","version":1}},"canonical_sha256":"a4246f1e791852f9c4ce23f72730f5695904f86c78a31f77b7ca10e4dfc59786","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a4246f1e791852f9c4ce23f72730f5695904f86c78a31f77b7ca10e4dfc59786","first_computed_at":"2026-05-18T01:07:54.729283Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:07:54.729283Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7Grd61PnDdh3rMoWgAia0K4aiiq6J9UZX50EH1sn0iE1xkd3BOO1FR3oA3vXVSW1xnDvZjGzyh0RFSm26l/HDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:07:54.729743Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.07138","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ac06332ea951b25f2f341369cefc6256ef77ee2ea9fed85391e6847e7609052f","sha256:d7007f3a29837ba053be088c4b7752cd9d3cf9d0d0288aedcd00ddd4a9772abd"],"state_sha256":"5aaa2cb274eb6429c670a46166cd5cbdb3a76c3a6eaa4d8a23c989825e7a30e5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jOTArXHQgf9vJaA2al/fKTVMwj/bkrRstqedq1zE07Erj13NUB0IUB2Nko6TsWSWyVymD5V4Wnb4WguI6bKoCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T19:50:24.139278Z","bundle_sha256":"450f658b61bfcb8357fffec1296a28080818b868b15227eed4b7558619966c3f"}}