{"paper":{"title":"Free-Grained Hierarchical Visual Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Hierarchical image recognition can learn consistent predictions from labels at any taxonomy level using mixed-granularity supervision.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Seulki Park, Stella X. Yu, Zilin Wang","submitted_at":"2025-10-16T14:35:18Z","abstract_excerpt":"Hierarchical image recognition seeks to predict class labels along a semantic taxonomy, from broad categories to specific ones, typically under the tidy assumption that every training image is fully annotated along its taxonomy path. Reality is messier: A distant bird may be labeled only bird, while a clear close-up may justify bald eagle. We introduce free-grain training, where labels may appear at any level of the taxonomy and models must learn consistent hierarchical predictions from incomplete, mixed-granularity supervision. We build benchmark datasets with varying label granularity and sh"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We introduce free-grain training, where labels may appear at any level of the taxonomy and models must learn consistent hierarchical predictions from incomplete, mixed-granularity supervision.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That supplementing with broad text-based supervision or framing missing labels as semi-supervised learning will sufficiently compensate for incomplete annotations without introducing new inconsistencies or performance drops.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Introduces free-grain hierarchical visual recognition with new benchmarks for mixed-granularity labels and methods using text-based supervision plus semi-supervised learning for missing labels.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Hierarchical image recognition can learn consistent predictions from labels at any taxonomy level using mixed-granularity supervision.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"ecc2df2a5f39c7120a0138065adc5771733e0b994f9e75424c5c98de4a7eab79"},"source":{"id":"2510.14737","kind":"arxiv","version":3},"verdict":{"id":"9ccae7e9-e5b9-4c86-837d-1e96404f9df3","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-18T06:05:51.368233Z","strongest_claim":"We introduce free-grain training, where labels may appear at any level of the taxonomy and models must learn consistent hierarchical predictions from incomplete, mixed-granularity supervision.","one_line_summary":"Introduces free-grain hierarchical visual recognition with new benchmarks for mixed-granularity labels and methods using text-based supervision plus semi-supervised learning for missing labels.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That supplementing with broad text-based supervision or framing missing labels as semi-supervised learning will sufficiently compensate for incomplete annotations without introducing new inconsistencies or performance drops.","pith_extraction_headline":"Hierarchical image recognition can learn consistent predictions from labels at any taxonomy level using mixed-granularity supervision."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2510.14737/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":2,"snapshot_sha256":"bdb70b7f470661db12862d79bbed1a1bff4b3215c171514a6023c4b44de56179"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}