GIG-CAM+M: A Class Activation Mapping Method Incorporating Guided Integrated Gradients and Multi-scale Strategy

The interpretability of convolutional neural networks has garnered widespread attention, with class activation mapping (CAM)-based methods emerging as a prominent research direction.Integrated Grad-CAM is a widely used backpropagation-based CAM method, but its use of a linear path introduces noise during the integration process.To address this issue, we propose GIG-CAM, which replaces the linear path with an adaptive path.Unlike previous methods that require path specification, GIG-CAM dynamically determines the next input in the path based on saliency maps.Additionally, to enhance the resolution of saliency maps, we introduce a novel multi-scale fusion method, which recursively optimizes saliency maps at smaller scales using saliency maps at larger scales.This preserves the localization capability of the originalscale saliency maps while enhancing their resolution.Experimental results on the VOC2012 and ILSVRC2012 datasets demonstrate that GIG-CAM with fusion (GIG-CAM(F)) outperforms existing methods, achieving the highest scores in the Pointing Game (82.80% and 85.90% on ResNet50 for VOC2012 and ILSVRC2012, respectively) and Energy-Based Pointing Game (62.41% and 59.69%, respectively).Furthermore, GIG-CAM(F) achieves the lowest Drop% (22.59% and 17.04%) and highest Increase% (31.00% and 21.95%), validating its superior interpretability.Our results highlight the effectiveness of GIG-CAM in improving the quality and reliability of saliency maps, making it a robust solution for enhancing deep model transparency.