PIP-PACA: An Interpretable Image Classification Framework via Prototype-Aware Clustering Attention

Image classification interpretability remains a fundamental challenge in the field of computer vision. Despite the remarkable improvements achieved by deep neural networks in classification accuracy, their decision-making processes are often opaque, which limits their applicability in high-stakes scenarios requiring reliability and transparency. Prototype-based methods, such as PIP-Net, address this issue by establishing explicit correspondences between input images and semantic prototypes, thereby enabling an intuitive, evidence-based reasoning paradigm. However, these approaches still suffer from insufficient global context modeling and underutilization of structural relationships among prototypes. To address these limitations, this paper proposes an interpretable image classification model termed PIP-PACA, which is built upon a prototype-aware clustering attention mechanism. In contrast to conventional Transformer architectures based on self-attention, the proposed PACA module introduces a set of learnable cluster centers to project feature representations into a prototype space. Global information is then captured via a bidirectional attention mechanism between features and prototypes. This design is inherently aligned with the principles of prototype learning while reducing the computational complexity from quadratic to linear. Furthermore, a normalization operation is incorporated during the feature extraction stage to enhance the stability of feature distributions and improve the reliability of prototype matching. Extensive experimental results demonstrate that the proposed method not only preserves the interpretability of the original framework but also achieves notable improvements in classification accuracy, sparsity, and prototype purity. These findings validate the effectiveness and superiority of the clustering-based attention mechanism within the prototype learning paradigm.