{"slug": "hira-cam-preserving-fine-grained-spatial-relevance-in-gradient-based-visual", "title": "HiRA-CAM: Preserving Fine-Grained Spatial Relevance in Gradient-Based Visual Explanations", "summary": "Researchers proposed HiRA-CAM, an improved gradient-based visual explanation method for convolutional neural networks (CNNs), and showed it outperforms LayerCAM and Grad-CAM on creating saliency maps for object classification. The method adaptively uses activation maps from all CNN layers to produce more focused saliency maps, addressing the challenge of explaining deep learning models with billions of parameters.", "body_md": "# Computer Science > Computer Vision and Pattern Recognition\n\n[Submitted on 19 Aug 2026]\n\n# Title:HiRA-CAM: Preserving Fine-Grained Spatial Relevance in Gradient-Based Visual Explanations\n\n[View PDF](/pdf/2608.19407)\n\n[HTML (experimental)](https://arxiv.org/html/2608.19407v1)\n\nAbstract: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.\n\n### Current browse context:\n\ncs.CV\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/hira-cam-preserving-fine-grained-spatial-relevance-in-gradient-based-visual", "canonical_source": "https://arxiv.org/abs/2608.19407", "published_at": "2026-08-21 04:00:00+00:00", "updated_at": "2026-08-21 04:16:37.763682+00:00", "lang": "en", "topics": ["computer-vision", "artificial-intelligence", "machine-learning"], "entities": ["HiRA-CAM", "LayerCAM", "Grad-CAM"], "alternates": {"html": "https://wpnews.pro/news/hira-cam-preserving-fine-grained-spatial-relevance-in-gradient-based-visual", "markdown": "https://wpnews.pro/news/hira-cam-preserving-fine-grained-spatial-relevance-in-gradient-based-visual.md", "text": "https://wpnews.pro/news/hira-cam-preserving-fine-grained-spatial-relevance-in-gradient-based-visual.txt", "jsonld": "https://wpnews.pro/news/hira-cam-preserving-fine-grained-spatial-relevance-in-gradient-based-visual.jsonld"}}