{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:A4GW7OS2VKGY3Y6IPTHYOI2VHW","short_pith_number":"pith:A4GW7OS2","canonical_record":{"source":{"id":"2410.00807","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-01T15:54:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7fa3518cffc4ac484cbb682b5fd96275bca0f44c1637162d014cbab1acf1edbd","abstract_canon_sha256":"3da4b69a56178808e86b2d9d4a3f42f7e85778e5c52aaf54952d54f3362d987d"},"schema_version":"1.0"},"canonical_sha256":"070d6fba5aaa8d8de3c87ccf8723553da397781c5d83447f132736082ec79375","source":{"kind":"arxiv","id":"2410.00807","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.00807","created_at":"2026-07-05T09:14:11Z"},{"alias_kind":"arxiv_version","alias_value":"2410.00807v1","created_at":"2026-07-05T09:14:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.00807","created_at":"2026-07-05T09:14:11Z"},{"alias_kind":"pith_short_12","alias_value":"A4GW7OS2VKGY","created_at":"2026-07-05T09:14:11Z"},{"alias_kind":"pith_short_16","alias_value":"A4GW7OS2VKGY3Y6I","created_at":"2026-07-05T09:14:11Z"},{"alias_kind":"pith_short_8","alias_value":"A4GW7OS2","created_at":"2026-07-05T09:14:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:A4GW7OS2VKGY3Y6IPTHYOI2VHW","target":"record","payload":{"canonical_record":{"source":{"id":"2410.00807","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-01T15:54:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7fa3518cffc4ac484cbb682b5fd96275bca0f44c1637162d014cbab1acf1edbd","abstract_canon_sha256":"3da4b69a56178808e86b2d9d4a3f42f7e85778e5c52aaf54952d54f3362d987d"},"schema_version":"1.0"},"canonical_sha256":"070d6fba5aaa8d8de3c87ccf8723553da397781c5d83447f132736082ec79375","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:14:11.129307Z","signature_b64":"ri5/tanh8j9gzltnq9T0qC7DM0Vh/1ZNw0+clia4G1RJrTayQmrg8JBuv5qpGuOMk6VSTvkIi3cSHCIwE9UYBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"070d6fba5aaa8d8de3c87ccf8723553da397781c5d83447f132736082ec79375","last_reissued_at":"2026-07-05T09:14:11.128924Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:14:11.128924Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.00807","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":"2026-07-05T09:14:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jEf/HaE7o/asVuVF91CxezrBgqPUNde6ub7HNW79itC/rXU25sXiOPOkXL5pSgUXI8NqyP5NAjTgirjJ3BmpBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T15:52:45.795610Z"},"content_sha256":"b6bcb8b3b99c45c262b5c8d29f9db7c2054ff03f92c4c4942d9441f321ca1e5e","schema_version":"1.0","event_id":"sha256:b6bcb8b3b99c45c262b5c8d29f9db7c2054ff03f92c4c4942d9441f321ca1e5e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:A4GW7OS2VKGY3Y6IPTHYOI2VHW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"WiGNet: Windowed Vision Graph Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Attilio Fiandrotti, Enzo Tartaglione, Gabriele Spadaro, Jhony H. Giraldo, Marco Grangetto","submitted_at":"2024-10-01T15:54:07Z","abstract_excerpt":"In recent years, Graph Neural Networks (GNNs) have demonstrated strong adaptability to various real-world challenges, with architectures such as Vision GNN (ViG) achieving state-of-the-art performance in several computer vision tasks. However, their practical applicability is hindered by the computational complexity of constructing the graph, which scales quadratically with the image size. In this paper, we introduce a novel Windowed vision Graph neural Network (WiGNet) model for efficient image processing. WiGNet explores a different strategy from previous works by partitioning the image into"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.00807","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/2410.00807/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":"2026-07-05T09:14:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HerhJhXI0EzdN2rfwKDyYA1oDQMoNvTUvlBk4Kgrxw+LZBCQgQCM1BX63qKgq5pC+dw8jcQxPxi3yIPtaNtUCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T15:52:45.796125Z"},"content_sha256":"2d171d63afa8e9d7355a18cdb873949a99e805df8bcab97a95d0e7dea7cc4ce8","schema_version":"1.0","event_id":"sha256:2d171d63afa8e9d7355a18cdb873949a99e805df8bcab97a95d0e7dea7cc4ce8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A4GW7OS2VKGY3Y6IPTHYOI2VHW/bundle.json","state_url":"https://pith.science/pith/A4GW7OS2VKGY3Y6IPTHYOI2VHW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A4GW7OS2VKGY3Y6IPTHYOI2VHW/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":"2026-08-05T15:52:45Z","links":{"resolver":"https://pith.science/pith/A4GW7OS2VKGY3Y6IPTHYOI2VHW","bundle":"https://pith.science/pith/A4GW7OS2VKGY3Y6IPTHYOI2VHW/bundle.json","state":"https://pith.science/pith/A4GW7OS2VKGY3Y6IPTHYOI2VHW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A4GW7OS2VKGY3Y6IPTHYOI2VHW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:A4GW7OS2VKGY3Y6IPTHYOI2VHW","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":"3da4b69a56178808e86b2d9d4a3f42f7e85778e5c52aaf54952d54f3362d987d","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-01T15:54:07Z","title_canon_sha256":"7fa3518cffc4ac484cbb682b5fd96275bca0f44c1637162d014cbab1acf1edbd"},"schema_version":"1.0","source":{"id":"2410.00807","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.00807","created_at":"2026-07-05T09:14:11Z"},{"alias_kind":"arxiv_version","alias_value":"2410.00807v1","created_at":"2026-07-05T09:14:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.00807","created_at":"2026-07-05T09:14:11Z"},{"alias_kind":"pith_short_12","alias_value":"A4GW7OS2VKGY","created_at":"2026-07-05T09:14:11Z"},{"alias_kind":"pith_short_16","alias_value":"A4GW7OS2VKGY3Y6I","created_at":"2026-07-05T09:14:11Z"},{"alias_kind":"pith_short_8","alias_value":"A4GW7OS2","created_at":"2026-07-05T09:14:11Z"}],"graph_snapshots":[{"event_id":"sha256:2d171d63afa8e9d7355a18cdb873949a99e805df8bcab97a95d0e7dea7cc4ce8","target":"graph","created_at":"2026-07-05T09:14:11Z","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/2410.00807/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, Graph Neural Networks (GNNs) have demonstrated strong adaptability to various real-world challenges, with architectures such as Vision GNN (ViG) achieving state-of-the-art performance in several computer vision tasks. However, their practical applicability is hindered by the computational complexity of constructing the graph, which scales quadratically with the image size. In this paper, we introduce a novel Windowed vision Graph neural Network (WiGNet) model for efficient image processing. WiGNet explores a different strategy from previous works by partitioning the image into","authors_text":"Attilio Fiandrotti, Enzo Tartaglione, Gabriele Spadaro, Jhony H. Giraldo, Marco Grangetto","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-01T15:54:07Z","title":"WiGNet: Windowed Vision Graph Neural Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.00807","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:b6bcb8b3b99c45c262b5c8d29f9db7c2054ff03f92c4c4942d9441f321ca1e5e","target":"record","created_at":"2026-07-05T09:14:11Z","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":"3da4b69a56178808e86b2d9d4a3f42f7e85778e5c52aaf54952d54f3362d987d","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-01T15:54:07Z","title_canon_sha256":"7fa3518cffc4ac484cbb682b5fd96275bca0f44c1637162d014cbab1acf1edbd"},"schema_version":"1.0","source":{"id":"2410.00807","kind":"arxiv","version":1}},"canonical_sha256":"070d6fba5aaa8d8de3c87ccf8723553da397781c5d83447f132736082ec79375","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"070d6fba5aaa8d8de3c87ccf8723553da397781c5d83447f132736082ec79375","first_computed_at":"2026-07-05T09:14:11.128924Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:14:11.128924Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ri5/tanh8j9gzltnq9T0qC7DM0Vh/1ZNw0+clia4G1RJrTayQmrg8JBuv5qpGuOMk6VSTvkIi3cSHCIwE9UYBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:14:11.129307Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.00807","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b6bcb8b3b99c45c262b5c8d29f9db7c2054ff03f92c4c4942d9441f321ca1e5e","sha256:2d171d63afa8e9d7355a18cdb873949a99e805df8bcab97a95d0e7dea7cc4ce8"],"state_sha256":"0b23f9bfc6a246639ce3216f626b57d00b2391d2185699e9d0ba9d0284e82342"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z9oahCp5vvMHbrngP0yQZM0rcaevaQYsgiLxU2xZvk1JU0RR/CyCu1Cd1dOHVk6h8j2EW70rl7/ET1UpqCkoBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T15:52:45.800543Z","bundle_sha256":"7666774aa7a286036335ad747ee1ed19fa836cd555a25247de5904ba1eefca13"}}