Grad-CAM is a widely used feature attribution method for visualizing class-relevant regions in image classification models, but its attribution maps often suffer from low spatial resolution. To address this problem, we propose an enhanced version of Grad-CAM that is integrated with FeatUp, a model-agnostic upsampling technique for constructing high-resolution feature maps. In FeatUp, we adopt the Implicit variant, which uses multi-layer perceptrons (MLPs) to generate continuous representations over arbitrary spatial coordinates. Whereas the original FeatUp paper briefly mentioned its potential for explainability in deep learning, our study conducted an in-depth investigation of its effectiveness. As a result, thanks to FeatUp, the proposed method successfully enhances the resolution of Grad-CAM without modifying the model architecture under study. We evaluated the proposed method with ViT, ResNet50, and VGG19 both qualitatively and quantitatively. Experimental results indicated a certain improvement in quantitative evaluation, and a notable improvement in qualitative evaluation, especially in terms of localization and visual clarity. These findings suggest that high-resolution feature representations can benefit feature attribution in image classification.