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原文摘要

Prototype-based methods enhance interpretability in image recognition by establishing intermediate part-level prototypes to build interpretable classifiers, enabling transparent decision-making through localized evidence and reference to prototypical examples. However, most existing methods typically depend on unimodal visual supervision and constrain prototypes within the visual embedding space, which inherently restricts their alignment with language-conditioned semantic representations. In this paper, we present PRISM (Prototype-based Reasoning with Inter-modal Semantic Mining), a framework for interpretable image recognition that leverages natural language as an auxiliary modality to guide the learning of class-specific part prototypes. PRISM introduces an information-theoretic attribution mechanism to extract semantically relevant image regions conditioned on textual descriptions. By aligning attribution maps with prototype activation patterns, PRISM implicitly anchors visual part prototypes to semantically meaningful image regions without requiring explicit concept annotations. To promote better localization and reduce redundancy among prototypes, we introduce a spatial compactness constraint that encourages each prototype to attend to non-overlapping and spatially concentrated regions. Experiments on multiple fine-grained benchmarks demonstrate that PRISM not only improves classification performance but also provides faithful and semantically grounded visual explanations.