{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:4EWFK2P6IQBB7G2M2EGHL772QI","short_pith_number":"pith:4EWFK2P6","schema_version":"1.0","canonical_sha256":"e12c5569fe44021f9b4cd10c75fffa8226763a5bb04430e7116c4295ed5f07eb","source":{"kind":"arxiv","id":"1811.10575","version":6},"attestation_state":"computed","paper":{"title":"Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ajay Divakaran, Larry S. Davis, Pallabi Ghosh, Yi Yao","submitted_at":"2018-11-26T18:28:24Z","abstract_excerpt":"We propose novel Stacked Spatio-Temporal Graph Convolutional Networks (Stacked-STGCN) for action segmentation, i.e., predicting and localizing a sequence of actions over long videos. We extend the Spatio-Temporal Graph Convolutional Network (STGCN) originally proposed for skeleton-based action recognition to enable nodes with different characteristics (e.g., scene, actor, object, action, etc.), feature descriptors with varied lengths, and arbitrary temporal edge connections to account for large graph deformation commonly associated with complex activities. We further introduce the stacked hour"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1811.10575","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T18:28:24Z","cross_cats_sorted":[],"title_canon_sha256":"3fa6d20664b2b45457d04a433bd7440065960b744aa93f64711fd644b79c8aae","abstract_canon_sha256":"3704b8c9b5575b4abb408f1ad847d826298c47687159f8ab199443e245a10ed2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:29.864992Z","signature_b64":"Ii/qHCwk0I6oVtavNFijLU3K80KJAiAxr4Kclzjly48VL28fn7VMf5lu+gigsYlCzkRXgfrHPlF9E5UVkQ+3Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e12c5569fe44021f9b4cd10c75fffa8226763a5bb04430e7116c4295ed5f07eb","last_reissued_at":"2026-05-17T23:44:29.864459Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:29.864459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ajay Divakaran, Larry S. Davis, Pallabi Ghosh, Yi Yao","submitted_at":"2018-11-26T18:28:24Z","abstract_excerpt":"We propose novel Stacked Spatio-Temporal Graph Convolutional Networks (Stacked-STGCN) for action segmentation, i.e., predicting and localizing a sequence of actions over long videos. We extend the Spatio-Temporal Graph Convolutional Network (STGCN) originally proposed for skeleton-based action recognition to enable nodes with different characteristics (e.g., scene, actor, object, action, etc.), feature descriptors with varied lengths, and arbitrary temporal edge connections to account for large graph deformation commonly associated with complex activities. We further introduce the stacked hour"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.10575","kind":"arxiv","version":6},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1811.10575","created_at":"2026-05-17T23:44:29.864560+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.10575v6","created_at":"2026-05-17T23:44:29.864560+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.10575","created_at":"2026-05-17T23:44:29.864560+00:00"},{"alias_kind":"pith_short_12","alias_value":"4EWFK2P6IQBB","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_16","alias_value":"4EWFK2P6IQBB7G2M","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_8","alias_value":"4EWFK2P6","created_at":"2026-05-18T12:32:05.422762+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4EWFK2P6IQBB7G2M2EGHL772QI","json":"https://pith.science/pith/4EWFK2P6IQBB7G2M2EGHL772QI.json","graph_json":"https://pith.science/api/pith-number/4EWFK2P6IQBB7G2M2EGHL772QI/graph.json","events_json":"https://pith.science/api/pith-number/4EWFK2P6IQBB7G2M2EGHL772QI/events.json","paper":"https://pith.science/paper/4EWFK2P6"},"agent_actions":{"view_html":"https://pith.science/pith/4EWFK2P6IQBB7G2M2EGHL772QI","download_json":"https://pith.science/pith/4EWFK2P6IQBB7G2M2EGHL772QI.json","view_paper":"https://pith.science/paper/4EWFK2P6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.10575&json=true","fetch_graph":"https://pith.science/api/pith-number/4EWFK2P6IQBB7G2M2EGHL772QI/graph.json","fetch_events":"https://pith.science/api/pith-number/4EWFK2P6IQBB7G2M2EGHL772QI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4EWFK2P6IQBB7G2M2EGHL772QI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4EWFK2P6IQBB7G2M2EGHL772QI/action/storage_attestation","attest_author":"https://pith.science/pith/4EWFK2P6IQBB7G2M2EGHL772QI/action/author_attestation","sign_citation":"https://pith.science/pith/4EWFK2P6IQBB7G2M2EGHL772QI/action/citation_signature","submit_replication":"https://pith.science/pith/4EWFK2P6IQBB7G2M2EGHL772QI/action/replication_record"}},"created_at":"2026-05-17T23:44:29.864560+00:00","updated_at":"2026-05-17T23:44:29.864560+00:00"}