待翻譯：HiRA-CAM: Preserving Fine-Grained Spatial Relevance in Gradient-Based Visual Explanations
AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：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.
AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
--> [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 View PDF HTML (experimental) 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) Full-text links: Access Paper: 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 View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-08 Change to browse by: cs cs.AI cs.LG cs.NE References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both 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. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)