{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:M2P2ND66IZE7TEFVX4W222XYIW","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":"a840b5338c92b99eb8446612f7a19211311064501277de6550553b296e17ff4b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-31T05:23:05Z","title_canon_sha256":"055e45ea7c1c72d3c538d8ada09d8a237c3a5a1a12e220335a4452a3fb5a0f6c"},"schema_version":"1.0","source":{"id":"1801.10304","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.10304","created_at":"2026-05-18T00:18:45Z"},{"alias_kind":"arxiv_version","alias_value":"1801.10304v2","created_at":"2026-05-18T00:18:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.10304","created_at":"2026-05-18T00:18:45Z"},{"alias_kind":"pith_short_12","alias_value":"M2P2ND66IZE7","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"M2P2ND66IZE7TEFV","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"M2P2ND66","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:785c2e15a57372a8eadadfa970f3978dc28e6a3e1547c65791022606cbc3c0be","target":"graph","created_at":"2026-05-18T00:18:45Z","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 with 3D skeleton sequences is becoming popular due to its speed and robustness. The recently proposed Convolutional Neural Networks (CNN) based methods have shown good performance in learning spatio-temporal representations for skeleton sequences. Despite the good recognition accuracy achieved by previous CNN based methods, there exist two problems that potentially limit the performance. First, previous skeleton representations are generated by chaining joints with a fixed order. The corresponding semantic meaning is unclear and the structural information among the joints is","authors_text":"Jianchao Yang, Jiebo Luo, Yuncheng Li, Zhengyuan Yang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-31T05:23:05Z","title":"Action Recognition with Spatio-Temporal Visual Attention on Skeleton Image Sequences"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.10304","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":"sha256:36b9707b3a3a4723c4b7d49d104e3d3abfb54945d1e4ea555101c0bf24848b87","target":"record","created_at":"2026-05-18T00:18:45Z","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":"a840b5338c92b99eb8446612f7a19211311064501277de6550553b296e17ff4b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-31T05:23:05Z","title_canon_sha256":"055e45ea7c1c72d3c538d8ada09d8a237c3a5a1a12e220335a4452a3fb5a0f6c"},"schema_version":"1.0","source":{"id":"1801.10304","kind":"arxiv","version":2}},"canonical_sha256":"669fa68fde4649f990b5bf2dad6af8458938f7eca8e46aec82e2c9e1ff6d334e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"669fa68fde4649f990b5bf2dad6af8458938f7eca8e46aec82e2c9e1ff6d334e","first_computed_at":"2026-05-18T00:18:45.795942Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:18:45.795942Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RipIBUAgBH94V1rotveH8P0b6jEjhqc6+wHxa5b2bQRHMboGBlDWwKavaL1U5aITvZhwc4kVQR5sm1+O5+JKCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:18:45.796476Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.10304","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:36b9707b3a3a4723c4b7d49d104e3d3abfb54945d1e4ea555101c0bf24848b87","sha256:785c2e15a57372a8eadadfa970f3978dc28e6a3e1547c65791022606cbc3c0be"],"state_sha256":"246087c3dd47161e1d08af1d92c4d1d4b7f82bae93d39dc688580340feeb13a2"}