{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4NWIO2QPUMXVZBJA6JHOWO573O","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":"b07281436e9ac75277ff3d24d5b02c342c2f7159f96cdd6e92b03304be325da7","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-09T00:36:02Z","title_canon_sha256":"04a4582ef897f71396909d55d7672f8d6891f9ac2b03ddb2e87866ce7a82318f"},"schema_version":"1.0","source":{"id":"2503.06368","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.06368","created_at":"2026-07-05T10:27:31Z"},{"alias_kind":"arxiv_version","alias_value":"2503.06368v1","created_at":"2026-07-05T10:27:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.06368","created_at":"2026-07-05T10:27:31Z"},{"alias_kind":"pith_short_12","alias_value":"4NWIO2QPUMXV","created_at":"2026-07-05T10:27:31Z"},{"alias_kind":"pith_short_16","alias_value":"4NWIO2QPUMXVZBJA","created_at":"2026-07-05T10:27:31Z"},{"alias_kind":"pith_short_8","alias_value":"4NWIO2QP","created_at":"2026-07-05T10:27:31Z"}],"graph_snapshots":[{"event_id":"sha256:685ce9dc631a068e74a72e305af41ea1a07e724ef6d2935c136db4c75660a919","target":"graph","created_at":"2026-07-05T10:27:31Z","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/2503.06368/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Texture recognition has recently been dominated by ImageNet-pre-trained deep Convolutional Neural Networks (CNNs), with specialized modifications and feature engineering required to achieve state-of-the-art (SOTA) performance. However, although Vision Transformers (ViTs) were introduced a few years ago, little is known about their texture recognition ability. Therefore, in this work, we introduce VORTEX (ViTs with Orderless and Randomized Token Encodings for Texture Recognition), a novel method that enables the effective use of ViTs for texture analysis. VORTEX extracts multi-depth token embed","authors_text":"Emir Konuk, Kallil M. Zielinski, Kevin Smith, Leonardo Scabini, Lucas C. Ribas, Odemir M. Bruno, Ricardo T. Fares","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-09T00:36:02Z","title":"VORTEX: Challenging CNNs at Texture Recognition by using Vision Transformers with Orderless and Randomized Token Encodings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.06368","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":"sha256:07a014f62f8ced2d13842430b95bcbc2891a696d66ea274ad7a8ac3c8c4c50b2","target":"record","created_at":"2026-07-05T10:27:31Z","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":"b07281436e9ac75277ff3d24d5b02c342c2f7159f96cdd6e92b03304be325da7","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-09T00:36:02Z","title_canon_sha256":"04a4582ef897f71396909d55d7672f8d6891f9ac2b03ddb2e87866ce7a82318f"},"schema_version":"1.0","source":{"id":"2503.06368","kind":"arxiv","version":1}},"canonical_sha256":"e36c876a0fa32f5c8520f24eeb3bbfdba6dd93dfdfc239c2caa841f8d6c50dac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e36c876a0fa32f5c8520f24eeb3bbfdba6dd93dfdfc239c2caa841f8d6c50dac","first_computed_at":"2026-07-05T10:27:31.706229Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:27:31.706229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KV1+McggEvF2sitMdmuRpgtTvlgXTYM+WvUxWWrIdyJ9Sh+FN7+gtaTuUBq2JUorqq5qqHpKpx2ZMUs4WKtSCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:27:31.706791Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.06368","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:07a014f62f8ced2d13842430b95bcbc2891a696d66ea274ad7a8ac3c8c4c50b2","sha256:685ce9dc631a068e74a72e305af41ea1a07e724ef6d2935c136db4c75660a919"],"state_sha256":"286c462c8aa9c86f09e1b31d4daf5d8e62353564a0cf3f4495f10939bad3fbe8"}