{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:PBUTI2CC2AWYJHQVA6NW73PJ45","short_pith_number":"pith:PBUTI2CC","canonical_record":{"source":{"id":"2206.00272","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-06-01T07:01:04Z","cross_cats_sorted":[],"title_canon_sha256":"d180fda46296189ae57c63f3f9edf3479c693174107e4e6732b3809a976f4aad","abstract_canon_sha256":"3fed43ef9d413a957a13d892d2b9585611dc16ac6f643be370c172797131e0f2"},"schema_version":"1.0"},"canonical_sha256":"7869346842d02d849e15079b6fede9e76fa41532a0b42755b3c49b3a5aeafa96","source":{"kind":"arxiv","id":"2206.00272","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.00272","created_at":"2026-07-05T05:13:07Z"},{"alias_kind":"arxiv_version","alias_value":"2206.00272v3","created_at":"2026-07-05T05:13:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.00272","created_at":"2026-07-05T05:13:07Z"},{"alias_kind":"pith_short_12","alias_value":"PBUTI2CC2AWY","created_at":"2026-07-05T05:13:07Z"},{"alias_kind":"pith_short_16","alias_value":"PBUTI2CC2AWYJHQV","created_at":"2026-07-05T05:13:07Z"},{"alias_kind":"pith_short_8","alias_value":"PBUTI2CC","created_at":"2026-07-05T05:13:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:PBUTI2CC2AWYJHQVA6NW73PJ45","target":"record","payload":{"canonical_record":{"source":{"id":"2206.00272","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-06-01T07:01:04Z","cross_cats_sorted":[],"title_canon_sha256":"d180fda46296189ae57c63f3f9edf3479c693174107e4e6732b3809a976f4aad","abstract_canon_sha256":"3fed43ef9d413a957a13d892d2b9585611dc16ac6f643be370c172797131e0f2"},"schema_version":"1.0"},"canonical_sha256":"7869346842d02d849e15079b6fede9e76fa41532a0b42755b3c49b3a5aeafa96","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:13:07.154291Z","signature_b64":"gmGMc01Nh4QDuZUI6hOxRZte+mog487K9lShzEVCvos5ney/AsnvCviHL63FcD5+njcN48fM9VqnLaDygjSfDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7869346842d02d849e15079b6fede9e76fa41532a0b42755b3c49b3a5aeafa96","last_reissued_at":"2026-07-05T05:13:07.153805Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:13:07.153805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.00272","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:13:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ssl/Zp/aHm/lU8n2CVylpc26RYaeaF5Nglpi2TP4S9HfsfCgGAQeGs4798KLEtm7EOgzdf9cee4TLdOVB5cnBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T11:01:30.842296Z"},"content_sha256":"438b7d4f1326747c6334f9180145ac1d73b684297d0365f8e4fff3b65b61f4a1","schema_version":"1.0","event_id":"sha256:438b7d4f1326747c6334f9180145ac1d73b684297d0365f8e4fff3b65b61f4a1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:PBUTI2CC2AWYJHQVA6NW73PJ45","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Vision GNN: An Image is Worth Graph of Nodes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Enhua Wu, Jianyuan Guo, Kai Han, Yehui Tang, Yunhe Wang","submitted_at":"2022-06-01T07:01:04Z","abstract_excerpt":"Network architecture plays a key role in the deep learning-based computer vision system. The widely-used convolutional neural network and transformer treat the image as a grid or sequence structure, which is not flexible to capture irregular and complex objects. In this paper, we propose to represent the image as a graph structure and introduce a new Vision GNN (ViG) architecture to extract graph-level feature for visual tasks. We first split the image to a number of patches which are viewed as nodes, and construct a graph by connecting the nearest neighbors. Based on the graph representation "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.00272","kind":"arxiv","version":3},"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/2206.00272/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":"2026-07-05T05:13:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mo5KwYm+Fc3fJ+In7lV20tHWjlDb2T6bxf4O5XuV+YX6qbTH8xhzsqR7OaucxlKApj3uqyOZh/qk/Rvktp8VDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T11:01:30.842801Z"},"content_sha256":"20a26e42aeb3e3b1b95376abf896e3749100b47fb7d7bfb3f51204e7abcbe2b6","schema_version":"1.0","event_id":"sha256:20a26e42aeb3e3b1b95376abf896e3749100b47fb7d7bfb3f51204e7abcbe2b6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PBUTI2CC2AWYJHQVA6NW73PJ45/bundle.json","state_url":"https://pith.science/pith/PBUTI2CC2AWYJHQVA6NW73PJ45/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PBUTI2CC2AWYJHQVA6NW73PJ45/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":"2026-08-05T11:01:30Z","links":{"resolver":"https://pith.science/pith/PBUTI2CC2AWYJHQVA6NW73PJ45","bundle":"https://pith.science/pith/PBUTI2CC2AWYJHQVA6NW73PJ45/bundle.json","state":"https://pith.science/pith/PBUTI2CC2AWYJHQVA6NW73PJ45/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PBUTI2CC2AWYJHQVA6NW73PJ45/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:PBUTI2CC2AWYJHQVA6NW73PJ45","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":"3fed43ef9d413a957a13d892d2b9585611dc16ac6f643be370c172797131e0f2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-06-01T07:01:04Z","title_canon_sha256":"d180fda46296189ae57c63f3f9edf3479c693174107e4e6732b3809a976f4aad"},"schema_version":"1.0","source":{"id":"2206.00272","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.00272","created_at":"2026-07-05T05:13:07Z"},{"alias_kind":"arxiv_version","alias_value":"2206.00272v3","created_at":"2026-07-05T05:13:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.00272","created_at":"2026-07-05T05:13:07Z"},{"alias_kind":"pith_short_12","alias_value":"PBUTI2CC2AWY","created_at":"2026-07-05T05:13:07Z"},{"alias_kind":"pith_short_16","alias_value":"PBUTI2CC2AWYJHQV","created_at":"2026-07-05T05:13:07Z"},{"alias_kind":"pith_short_8","alias_value":"PBUTI2CC","created_at":"2026-07-05T05:13:07Z"}],"graph_snapshots":[{"event_id":"sha256:20a26e42aeb3e3b1b95376abf896e3749100b47fb7d7bfb3f51204e7abcbe2b6","target":"graph","created_at":"2026-07-05T05:13:07Z","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/2206.00272/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Network architecture plays a key role in the deep learning-based computer vision system. The widely-used convolutional neural network and transformer treat the image as a grid or sequence structure, which is not flexible to capture irregular and complex objects. In this paper, we propose to represent the image as a graph structure and introduce a new Vision GNN (ViG) architecture to extract graph-level feature for visual tasks. We first split the image to a number of patches which are viewed as nodes, and construct a graph by connecting the nearest neighbors. Based on the graph representation ","authors_text":"Enhua Wu, Jianyuan Guo, Kai Han, Yehui Tang, Yunhe Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-06-01T07:01:04Z","title":"Vision GNN: An Image is Worth Graph of Nodes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.00272","kind":"arxiv","version":3},"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":"sha256:438b7d4f1326747c6334f9180145ac1d73b684297d0365f8e4fff3b65b61f4a1","target":"record","created_at":"2026-07-05T05:13:07Z","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":"3fed43ef9d413a957a13d892d2b9585611dc16ac6f643be370c172797131e0f2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-06-01T07:01:04Z","title_canon_sha256":"d180fda46296189ae57c63f3f9edf3479c693174107e4e6732b3809a976f4aad"},"schema_version":"1.0","source":{"id":"2206.00272","kind":"arxiv","version":3}},"canonical_sha256":"7869346842d02d849e15079b6fede9e76fa41532a0b42755b3c49b3a5aeafa96","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7869346842d02d849e15079b6fede9e76fa41532a0b42755b3c49b3a5aeafa96","first_computed_at":"2026-07-05T05:13:07.153805Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:13:07.153805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gmGMc01Nh4QDuZUI6hOxRZte+mog487K9lShzEVCvos5ney/AsnvCviHL63FcD5+njcN48fM9VqnLaDygjSfDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:13:07.154291Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.00272","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:438b7d4f1326747c6334f9180145ac1d73b684297d0365f8e4fff3b65b61f4a1","sha256:20a26e42aeb3e3b1b95376abf896e3749100b47fb7d7bfb3f51204e7abcbe2b6"],"state_sha256":"bce10e5e3b0e39aeee7706677292034dbd5f36e24fbee76dfbff8f1ce5612417"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PjHsklXBATp2wy5JF9NuAbIeWc2q6mxebd8jYTu1MT/GuVE2qH62/zMVR6eRm9EmPc7icBN/NL9jEeXp/NlWCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T11:01:30.846313Z","bundle_sha256":"0399a668f9f890cd72d6bd057a02e85f7da3a360007678f1acf01f83ad3d1784"}}