{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:EVQ5ZK5W3FJNNBF5LPNZQBH5HK","short_pith_number":"pith:EVQ5ZK5W","canonical_record":{"source":{"id":"2002.07442","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-18T09:27:41Z","cross_cats_sorted":[],"title_canon_sha256":"f21a37ca4108f646bacb77b1c90e868ed3cfaf5ad14a002027ac56ef2d058d17","abstract_canon_sha256":"1b416fbca78b65f4abdaf470ce66551394626ba4d54aac142f7dfa2eccac0e6d"},"schema_version":"1.0"},"canonical_sha256":"2561dcabb6d952d684bd5bdb9804fd3aa8268b96aa9bab2e2bc0d2326baad92f","source":{"kind":"arxiv","id":"2002.07442","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.07442","created_at":"2026-07-05T00:42:02Z"},{"alias_kind":"arxiv_version","alias_value":"2002.07442v1","created_at":"2026-07-05T00:42:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.07442","created_at":"2026-07-05T00:42:02Z"},{"alias_kind":"pith_short_12","alias_value":"EVQ5ZK5W3FJN","created_at":"2026-07-05T00:42:02Z"},{"alias_kind":"pith_short_16","alias_value":"EVQ5ZK5W3FJNNBF5","created_at":"2026-07-05T00:42:02Z"},{"alias_kind":"pith_short_8","alias_value":"EVQ5ZK5W","created_at":"2026-07-05T00:42:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:EVQ5ZK5W3FJNNBF5LPNZQBH5HK","target":"record","payload":{"canonical_record":{"source":{"id":"2002.07442","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-18T09:27:41Z","cross_cats_sorted":[],"title_canon_sha256":"f21a37ca4108f646bacb77b1c90e868ed3cfaf5ad14a002027ac56ef2d058d17","abstract_canon_sha256":"1b416fbca78b65f4abdaf470ce66551394626ba4d54aac142f7dfa2eccac0e6d"},"schema_version":"1.0"},"canonical_sha256":"2561dcabb6d952d684bd5bdb9804fd3aa8268b96aa9bab2e2bc0d2326baad92f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:42:02.085207Z","signature_b64":"qP+FEv6EiLuBF6ynSE4VG/89BQqGjdrI8MVHn7IjJqrCS22GKpj28KwLzgYWEsoIAocoDF+m9Fy9TM4b7xbGAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2561dcabb6d952d684bd5bdb9804fd3aa8268b96aa9bab2e2bc0d2326baad92f","last_reissued_at":"2026-07-05T00:42:02.084713Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:42:02.084713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.07442","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":"2026-07-05T00:42:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6u3hngLU3nqobHmZ/Dz6zWxLgJn9adIvn2iCoSTjAFpDlFipVR+oynhxxG5pDlRshoYuZB4VS+S+1HR7ml3xCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T15:11:30.202020Z"},"content_sha256":"4c0fbb2ba92c7f19a551141eafdb8009134576af1f3c7b22d40fd24934213c83","schema_version":"1.0","event_id":"sha256:4c0fbb2ba92c7f19a551141eafdb8009134576af1f3c7b22d40fd24934213c83"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:EVQ5ZK5W3FJNNBF5LPNZQBH5HK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"V4D:4D Convolutional Neural Networks for Video-level Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Limin Wang, Matthew R. Scott, Sheng Guo, Shiwen Zhang, Weilin Huang","submitted_at":"2020-02-18T09:27:41Z","abstract_excerpt":"Most existing 3D CNNs for video representation learning are clip-based methods, and thus do not consider video-level temporal evolution of spatio-temporal features. In this paper, we propose Video-level 4D Convolutional Neural Networks, referred as V4D, to model the evolution of long-range spatio-temporal representation with 4D convolutions, and at the same time, to preserve strong 3D spatio-temporal representation with residual connections. Specifically, we design a new 4D residual block able to capture inter-clip interactions, which could enhance the representation power of the original clip"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.07442","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2002.07442/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":"2026-07-05T00:42:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H5avJfICLpyi7CSReOX2f8K5u9dogmJwZQFmro5ul7j3E7MGVMRH7X6IJ/e05m8RH22yjS+X4QYhjRJtjQeOCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T15:11:30.202351Z"},"content_sha256":"46fb9672f3ebff50254844a38dd59e61d601852205f690cfe03758dcdf8992f4","schema_version":"1.0","event_id":"sha256:46fb9672f3ebff50254844a38dd59e61d601852205f690cfe03758dcdf8992f4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EVQ5ZK5W3FJNNBF5LPNZQBH5HK/bundle.json","state_url":"https://pith.science/pith/EVQ5ZK5W3FJNNBF5LPNZQBH5HK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EVQ5ZK5W3FJNNBF5LPNZQBH5HK/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":"2026-08-22T15:11:30Z","links":{"resolver":"https://pith.science/pith/EVQ5ZK5W3FJNNBF5LPNZQBH5HK","bundle":"https://pith.science/pith/EVQ5ZK5W3FJNNBF5LPNZQBH5HK/bundle.json","state":"https://pith.science/pith/EVQ5ZK5W3FJNNBF5LPNZQBH5HK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EVQ5ZK5W3FJNNBF5LPNZQBH5HK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:EVQ5ZK5W3FJNNBF5LPNZQBH5HK","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":"1b416fbca78b65f4abdaf470ce66551394626ba4d54aac142f7dfa2eccac0e6d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-18T09:27:41Z","title_canon_sha256":"f21a37ca4108f646bacb77b1c90e868ed3cfaf5ad14a002027ac56ef2d058d17"},"schema_version":"1.0","source":{"id":"2002.07442","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.07442","created_at":"2026-07-05T00:42:02Z"},{"alias_kind":"arxiv_version","alias_value":"2002.07442v1","created_at":"2026-07-05T00:42:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.07442","created_at":"2026-07-05T00:42:02Z"},{"alias_kind":"pith_short_12","alias_value":"EVQ5ZK5W3FJN","created_at":"2026-07-05T00:42:02Z"},{"alias_kind":"pith_short_16","alias_value":"EVQ5ZK5W3FJNNBF5","created_at":"2026-07-05T00:42:02Z"},{"alias_kind":"pith_short_8","alias_value":"EVQ5ZK5W","created_at":"2026-07-05T00:42:02Z"}],"graph_snapshots":[{"event_id":"sha256:46fb9672f3ebff50254844a38dd59e61d601852205f690cfe03758dcdf8992f4","target":"graph","created_at":"2026-07-05T00:42:02Z","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/2002.07442/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most existing 3D CNNs for video representation learning are clip-based methods, and thus do not consider video-level temporal evolution of spatio-temporal features. In this paper, we propose Video-level 4D Convolutional Neural Networks, referred as V4D, to model the evolution of long-range spatio-temporal representation with 4D convolutions, and at the same time, to preserve strong 3D spatio-temporal representation with residual connections. Specifically, we design a new 4D residual block able to capture inter-clip interactions, which could enhance the representation power of the original clip","authors_text":"Limin Wang, Matthew R. Scott, Sheng Guo, Shiwen Zhang, Weilin Huang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-18T09:27:41Z","title":"V4D:4D Convolutional Neural Networks for Video-level Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.07442","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":"sha256:4c0fbb2ba92c7f19a551141eafdb8009134576af1f3c7b22d40fd24934213c83","target":"record","created_at":"2026-07-05T00:42:02Z","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":"1b416fbca78b65f4abdaf470ce66551394626ba4d54aac142f7dfa2eccac0e6d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-18T09:27:41Z","title_canon_sha256":"f21a37ca4108f646bacb77b1c90e868ed3cfaf5ad14a002027ac56ef2d058d17"},"schema_version":"1.0","source":{"id":"2002.07442","kind":"arxiv","version":1}},"canonical_sha256":"2561dcabb6d952d684bd5bdb9804fd3aa8268b96aa9bab2e2bc0d2326baad92f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2561dcabb6d952d684bd5bdb9804fd3aa8268b96aa9bab2e2bc0d2326baad92f","first_computed_at":"2026-07-05T00:42:02.084713Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:42:02.084713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qP+FEv6EiLuBF6ynSE4VG/89BQqGjdrI8MVHn7IjJqrCS22GKpj28KwLzgYWEsoIAocoDF+m9Fy9TM4b7xbGAA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:42:02.085207Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.07442","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c0fbb2ba92c7f19a551141eafdb8009134576af1f3c7b22d40fd24934213c83","sha256:46fb9672f3ebff50254844a38dd59e61d601852205f690cfe03758dcdf8992f4"],"state_sha256":"67ffe88099f2053a5289b788559456090ae1812a9d84f39c6d71d090210ce42b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qznhm0n6Aa3nr+tGFHuFoMp2XjpkdgKEyzSgryG2lao2FzdO0fnFIbaJYZGLKmtvXJxpgW8Wn6IZJi5pWJxIBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T15:11:30.206012Z","bundle_sha256":"c53f2012be6ec80cda4229f424f7c94c031f760da29f7b03e978d23851cde877"}}