{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:7DTSECAN72IZ47DVO3GVIXB4Y7","short_pith_number":"pith:7DTSECAN","canonical_record":{"source":{"id":"2107.12213","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-07-26T13:37:50Z","cross_cats_sorted":[],"title_canon_sha256":"bd2bbea752ee92b1457ea43bd775df90c33e27cf95466eb233cde6ea2d38fceb","abstract_canon_sha256":"072c41cf031445629f92c7cd583730b8f221d48bd44e8c69d7e5675ba938f33a"},"schema_version":"1.0"},"canonical_sha256":"f8e722080dfe919e7c7576cd545c3cc7f2a00559d289043807fa689487256aeb","source":{"kind":"arxiv","id":"2107.12213","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.12213","created_at":"2026-07-05T03:07:50Z"},{"alias_kind":"arxiv_version","alias_value":"2107.12213v2","created_at":"2026-07-05T03:07:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.12213","created_at":"2026-07-05T03:07:50Z"},{"alias_kind":"pith_short_12","alias_value":"7DTSECAN72IZ","created_at":"2026-07-05T03:07:50Z"},{"alias_kind":"pith_short_16","alias_value":"7DTSECAN72IZ47DV","created_at":"2026-07-05T03:07:50Z"},{"alias_kind":"pith_short_8","alias_value":"7DTSECAN","created_at":"2026-07-05T03:07:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:7DTSECAN72IZ47DVO3GVIXB4Y7","target":"record","payload":{"canonical_record":{"source":{"id":"2107.12213","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-07-26T13:37:50Z","cross_cats_sorted":[],"title_canon_sha256":"bd2bbea752ee92b1457ea43bd775df90c33e27cf95466eb233cde6ea2d38fceb","abstract_canon_sha256":"072c41cf031445629f92c7cd583730b8f221d48bd44e8c69d7e5675ba938f33a"},"schema_version":"1.0"},"canonical_sha256":"f8e722080dfe919e7c7576cd545c3cc7f2a00559d289043807fa689487256aeb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:07:50.756920Z","signature_b64":"ufRnZ1U1b6GkqaPB6dPPWlNI/y8bd9eCIM0eRURXV6iOGZODHRb15CLu/TEM9Bgm78UjTNG3Dl7NE+9s/V+eBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f8e722080dfe919e7c7576cd545c3cc7f2a00559d289043807fa689487256aeb","last_reissued_at":"2026-07-05T03:07:50.756565Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:07:50.756565Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.12213","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":"2026-07-05T03:07:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EVrqh9e4A6ywxqnfb+0T5yW1yD3r1RDe3X6EpyYrYhCgEE+opx3ibeeDP9tq31k7bMImur3d1SCyajDd3AI5Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T10:30:39.742394Z"},"content_sha256":"6d62ea044495fe7b79300dcce547b54a5fa0895604b62338961dc4f7650ebc92","schema_version":"1.0","event_id":"sha256:6d62ea044495fe7b79300dcce547b54a5fa0895604b62338961dc4f7650ebc92"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:7DTSECAN72IZ47DVO3GVIXB4Y7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Li, Chunfeng Yuan, Weiming Hu, Ying Deng, Yuxin Chen, Ziqi Zhang","submitted_at":"2021-07-26T13:37:50Z","abstract_excerpt":"Graph convolutional networks (GCNs) have been widely used and achieved remarkable results in skeleton-based action recognition. In GCNs, graph topology dominates feature aggregation and therefore is the key to extracting representative features. In this work, we propose a novel Channel-wise Topology Refinement Graph Convolution (CTR-GC) to dynamically learn different topologies and effectively aggregate joint features in different channels for skeleton-based action recognition. The proposed CTR-GC models channel-wise topologies through learning a shared topology as a generic prior for all chan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.12213","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2107.12213/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T03:07:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N792a80FlAL17eBRYVud/5mP4ZU7oaTS80L1V1wsrW10sZVd1dwSF8qlaLRCWxucg/RQ4+FSLyGxk2PxJwpgAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T10:30:39.742902Z"},"content_sha256":"e396cf4005b1f4fe66b48d8bed914d81effd003b5996e1f45f656edd467e6d15","schema_version":"1.0","event_id":"sha256:e396cf4005b1f4fe66b48d8bed914d81effd003b5996e1f45f656edd467e6d15"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7DTSECAN72IZ47DVO3GVIXB4Y7/bundle.json","state_url":"https://pith.science/pith/7DTSECAN72IZ47DVO3GVIXB4Y7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7DTSECAN72IZ47DVO3GVIXB4Y7/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":"2026-08-06T10:30:39Z","links":{"resolver":"https://pith.science/pith/7DTSECAN72IZ47DVO3GVIXB4Y7","bundle":"https://pith.science/pith/7DTSECAN72IZ47DVO3GVIXB4Y7/bundle.json","state":"https://pith.science/pith/7DTSECAN72IZ47DVO3GVIXB4Y7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7DTSECAN72IZ47DVO3GVIXB4Y7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:7DTSECAN72IZ47DVO3GVIXB4Y7","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":"072c41cf031445629f92c7cd583730b8f221d48bd44e8c69d7e5675ba938f33a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-07-26T13:37:50Z","title_canon_sha256":"bd2bbea752ee92b1457ea43bd775df90c33e27cf95466eb233cde6ea2d38fceb"},"schema_version":"1.0","source":{"id":"2107.12213","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.12213","created_at":"2026-07-05T03:07:50Z"},{"alias_kind":"arxiv_version","alias_value":"2107.12213v2","created_at":"2026-07-05T03:07:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.12213","created_at":"2026-07-05T03:07:50Z"},{"alias_kind":"pith_short_12","alias_value":"7DTSECAN72IZ","created_at":"2026-07-05T03:07:50Z"},{"alias_kind":"pith_short_16","alias_value":"7DTSECAN72IZ47DV","created_at":"2026-07-05T03:07:50Z"},{"alias_kind":"pith_short_8","alias_value":"7DTSECAN","created_at":"2026-07-05T03:07:50Z"}],"graph_snapshots":[{"event_id":"sha256:e396cf4005b1f4fe66b48d8bed914d81effd003b5996e1f45f656edd467e6d15","target":"graph","created_at":"2026-07-05T03:07:50Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2107.12213/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph convolutional networks (GCNs) have been widely used and achieved remarkable results in skeleton-based action recognition. In GCNs, graph topology dominates feature aggregation and therefore is the key to extracting representative features. In this work, we propose a novel Channel-wise Topology Refinement Graph Convolution (CTR-GC) to dynamically learn different topologies and effectively aggregate joint features in different channels for skeleton-based action recognition. The proposed CTR-GC models channel-wise topologies through learning a shared topology as a generic prior for all chan","authors_text":"Bing Li, Chunfeng Yuan, Weiming Hu, Ying Deng, Yuxin Chen, Ziqi Zhang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-07-26T13:37:50Z","title":"Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.12213","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":"sha256:6d62ea044495fe7b79300dcce547b54a5fa0895604b62338961dc4f7650ebc92","target":"record","created_at":"2026-07-05T03:07:50Z","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":"072c41cf031445629f92c7cd583730b8f221d48bd44e8c69d7e5675ba938f33a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-07-26T13:37:50Z","title_canon_sha256":"bd2bbea752ee92b1457ea43bd775df90c33e27cf95466eb233cde6ea2d38fceb"},"schema_version":"1.0","source":{"id":"2107.12213","kind":"arxiv","version":2}},"canonical_sha256":"f8e722080dfe919e7c7576cd545c3cc7f2a00559d289043807fa689487256aeb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f8e722080dfe919e7c7576cd545c3cc7f2a00559d289043807fa689487256aeb","first_computed_at":"2026-07-05T03:07:50.756565Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:07:50.756565Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ufRnZ1U1b6GkqaPB6dPPWlNI/y8bd9eCIM0eRURXV6iOGZODHRb15CLu/TEM9Bgm78UjTNG3Dl7NE+9s/V+eBw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:07:50.756920Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.12213","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6d62ea044495fe7b79300dcce547b54a5fa0895604b62338961dc4f7650ebc92","sha256:e396cf4005b1f4fe66b48d8bed914d81effd003b5996e1f45f656edd467e6d15"],"state_sha256":"e25efb2deb12dde2141f9f1ea182c7e6bc16bf4daa136532764f205c872d39be"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Sv3QKEVKPxegYyj3XQDDJlYzLCWzDJalToS2r7BdrhXLxUNQ1A3FOEu9R82QQud5O/qimoQ1cVPfGe06Go+gBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T10:30:39.746647Z","bundle_sha256":"491b8f7be291e19c9947795f70d48bcbb4307046a9711248578fe10cb8496754"}}