HiRA-CAM: Preserving Fine-Grained Spatial Relevance in Gradient-Based Visual Explanations
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Computer Science > Computer Vision and Pattern Recognition
Title:HiRA-CAM: Preserving Fine-Grained Spatial Relevance in Gradient-Based Visual Explanations
Abstract:Deep Learning models can include billions of parameters or more, making it difficult to explain their internal transformations and outputs. However, explainability is increasing in importance due to the use of AI in crucial applications. This paper focuses on the interpretability of convolutional neural networks (CNNs). Building on the popular gradient based method LayerCAM for extracting internal features in CNNs, we propose an improved method named HiRA-CAM, and show that it outperforms both LayerCAM and Grad-CAM on creating useful saliency maps for object classification. The main feature of HiRA-CAM is its adaptive use of activation maps from all the layers of the CNN to arrive at a more focused saliency map.
| Comments: | IEEE World Congress on Computational Intelligence, Maastricht, Netherlands, June 2026 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE) |
| ACM classes: | I.4.10; I.4.7; I.4.8; I.2.10 |
| Cite as: | arXiv:2608.19407 [cs.CV] |
| (or arXiv:2608.19407v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2608.19407
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