Fusion-CAM: Integrating Gradient and Region-Based Class Activation Maps for Robust Visual Explanations

TL;DR

Fusion-CAM combines gradient-based and region-based class activation maps through a novel fusion mechanism, producing more robust, detailed, and context-aware visual explanations for deep neural networks.

Contribution

The paper introduces Fusion-CAM, a new framework that unifies gradient and region-based CAM methods with an adaptive fusion mechanism for improved interpretability.

Findings

Fusion-CAM outperforms existing CAM methods in visualization quality.

The adaptive fusion enhances class coverage and explanation robustness.

Fusion-CAM provides more accurate and context-aware visual explanations.

Abstract

Interpreting the decision-making process of deep convolutional neural networks remains a central challenge in achieving trustworthy and transparent artificial intelligence. Explainable AI (XAI) techniques, particularly Class Activation Map (CAM) methods, are widely adopted to visualize the input regions influencing model predictions. Gradient-based approaches (e.g. Grad-CAM) provide highly discriminative, fine-grained details by computing gradients of class activations but often yield noisy and incomplete maps that emphasize only the most salient regions rather than the complete objects. Region-based approaches (e.g. Score-CAM) aggregate information over larger areas, capturing broader object coverage at the cost of over-smoothing and reduced sensitivity to subtle features. We introduce Fusion-CAM, a novel framework that bridges this explanatory gap by unifying both paradigms through a dedicated fusion mechanism to produce robust and highly discriminative visual explanations. Our method first denoises gradient-based maps, yielding cleaner and more focused activations. It then combines the refined gradient map with region-based maps using contribution weights to enhance class coverage. Finally, we propose an adaptive similarity-based pixel-level fusion that evaluates the agreement between both paradigms and dynamically adjusts the fusion strength. This adaptive mechanism reinforces consistent activations while softly blending conflicting regions, resulting in richer, context-aware, and input-adaptive visual explanations. Extensive experiments on standard benchmarks show that Fusion-CAM consistently outperforms existing CAM variants in both qualitative visualization and quantitative evaluation, providing a robust and flexible tool for interpreting deep neural networks.