HiRA-CAM: Preserving Fine-Grained Spatial Relevance in Gradient-Based Visual Explanations
arXiv:2608.19407v1 Announce Type: new 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.
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[Submitted on 19 Aug 2026]
Title:HiRA-CAM: Preserving Fine-Grained Spatial Relevance in Gradient-Based Visual Explanations
View a PDF of the paper titled HiRA-CAM: Preserving Fine-Grained Spatial Relevance in Gradient-Based Visual Explanations, by Manasi Nerurkar and 1 other authors
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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
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ali Minai [view email] [v1] Wed, 19 Aug 2026 19:44:43 UTC (27,579 KB)
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