AdvancedScoreCAM: Enhancing visual explainability through hierarchical upsampling

Abstract

Deep learning models have achieved remarkable success across various domains. However, the intricate nature of these models often hinders our understanding of their decision-making processes. Explainable AI methods such as Class Activation Mapping (CAM) become indispensable in providing intuitive explanations for these model decisions. Previous CAM-based methods often employed simple upsampling operations, resulting in the loss of contextual information. In this work, we propose a simple yet highly effective approach, AdvancedScoreCAM (ASC), which introduces a concurrent upsampling and fusion pipeline method to enhance visual explainability. Our proposed method introduces a direct and progressive upsampling pipeline, which can fully extracts contextual information during the upsampling process. This improvement is achieved by selectively integrating contextual details within the upsampled activation layers. Through extensive experiments and qualitative comparisons on two datasets, we demonstrate that ASC consistently produces clearer and more interpretable heatmaps that better reflect the model’s decision-making process compared to previous methods. Our code is available at https://github.com/jiiaozi/AdvancedScoreCAM.