DINO-QPM adapts frozen DINOv2 models via average-pooled patch embeddings and a sparsity loss to deliver both higher classification accuracy and human-interpretable global explanations.
Unless otherwise specified, the Multi-Layer Perceptron (MLP) consists of four layers featuring ReLU activation and batch nor- malisation
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DINO-QPM: Adapting Visual Foundation Models for Globally Interpretable Image Classification
DINO-QPM adapts frozen DINOv2 models via average-pooled patch embeddings and a sparsity loss to deliver both higher classification accuracy and human-interpretable global explanations.