{"ID":2896271,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.07993","arxiv_id":"2507.07993","title":"Multigranular Evaluation for Brain Visual Decoding","abstract":"Existing evaluation protocols for brain visual decoding predominantly rely on coarse metrics that obscure inter-model differences, lack neuroscientific foundation, and fail to capture fine-grained visual distinctions. To address these limitations, we introduce BASIC, a unified, multigranular evaluation framework that jointly quantifies structural fidelity, inferential alignment, and contextual coherence between decoded and ground-truth images. For the structural level, we introduce a hierarchical suite of segmentation-based metrics, including foreground, semantic, instance, and component masks, anchored in granularity-aware correspondence across mask structures. For the semantic level, we extract structured scene representations encompassing objects, attributes, and relationships using multimodal large language models, enabling detailed, scalable, and context-rich comparisons with ground-truth stimuli. We benchmark a diverse set of visual decoding methods across multiple stimulus-neuroimaging datasets within this unified evaluation framework. Together, these criteria provide a more discriminative, interpretable, and comprehensive foundation for evaluating brain visual decoding methods.","short_abstract":"Existing evaluation protocols for brain visual decoding predominantly rely on coarse metrics that obscure inter-model differences, lack neuroscientific foundation, and fail to capture fine-grained visual distinctions. To address these limitations, we introduce BASIC, a unified, multigranular evaluation framework that j...","url_abs":"https://arxiv.org/abs/2507.07993","url_pdf":"https://arxiv.org/pdf/2507.07993v2","authors":"[\"Weihao Xia\",\"Cengiz Oztireli\"]","published":"2025-07-10T17:59:24Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"eess.IV\",\"q-bio.NC\"]","methods":"[\"Language Model\"]","has_code":false}