标题： 多粒度评估用于大脑视觉解码 显示英文标题

标题： Multigranular Evaluation for Brain Visual Decoding

摘要： 现有的脑视觉解码评估协议主要依赖于粗粒度指标，这些指标掩盖了模型间的差异，缺乏神经科学基础，并且无法捕捉精细的视觉区别。 为解决这些局限性，我们引入了BASIC，这是一个统一的多粒度评估框架，能够联合量化解码图像与真实图像之间的结构保真度、推断对齐度和上下文连贯性。 在结构层面，我们引入了一套分层的基于分割的指标，包括前景、语义、实例和组件掩码，这些指标基于跨掩码结构的粒度感知对应关系。 在语义层面，我们使用多模态大语言模型提取包含物体、属性和关系的结构化场景表示，从而实现与真实刺激的详细、可扩展且富含上下文的比较。 我们在这一统一的评估框架内对多种视觉解码方法进行了基准测试。 这些标准共同提供了一个更具区分性、可解释性和全面性的基础，用于衡量脑视觉解码方法。 显示英文摘要

摘要： 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 measuring brain visual decoding methods.