Increment-CAM: Incrementally-Weighted Class Activation Maps for Better Visual Explanations

Abstract

Class activation maps (CAMs) are powerful tools for better understanding what convolutional neural networks learn and the reliability of their learning capability within relevant contexts. Highly inspired by Grad-CAM and Score-CAM, this manuscript proposes a novel method called Increment-CAM, which overcomes the limitations of both methods and enhances the localization capability of heatmaps, suppressing false activations to provide better discriminative visualizations. This paper offers a three-phase approach, generating activations through Grad-CAM in Phase 1 and applying a Score-CAM-like strategy to the Grad-CAM activation in Phase 2 with the difference that the input image is downsampled to the size of Grad-CAM activations to boost the computational process significantly. Finally, in Phase 3, the activations obtained in Phase 1 are combined with those generated in Phase 2, followed by regularizing the weighted sum. The steps involved in the Increment-CAM approach include Grad-CAM computation, Score-CAM computation with a modified approach, heatmap merging, and regularizing to obtain the final activation map. The proposed approach offers improved localization capabilities, better visualizations, and reduced false activations, providing fast, more accurate and interpretable visualizations of critical regions in an image. The paper also describes the experimental setup, evaluation metrics, and results of the proposed method on ImageNet, PascalVOC2007 and Caltech-UCSD Birds-200-2011 datasets, demonstrating its effectiveness compared to the existing visualization techniques.

Keywords

• Class activation maps
• convolutional neural networks
• heatmaps
• visual representations
• interpretability
• explainable AI