# Empowering CAM-Based Methods with Capability to Generate Fine-Grained and High-Faithfulness Explanations

arXiv: https://arxiv.org/abs/2303.09171 · HTML: https://mlsanity.com/paper/2303.09171/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 35 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 26 of 26)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Top-down Neural Attention by Excitation Backprop](https://mlsanity.com/paper/1608.00507/) (2016)
- [Explaining NonLinear Classification Decisions with Deep Taylor Decomposition](https://mlsanity.com/paper/1512.02479/) (2015)
- [Real Time Image Saliency for Black Box Classifiers](https://mlsanity.com/paper/1705.07857/) (2017)
- [The Shattered Gradients Problem: If resnets are the answer, then what is the question?](https://mlsanity.com/paper/1702.08591/) (2017)
- [Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs](https://mlsanity.com/paper/2008.02312/) (2020)
- [TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization](https://mlsanity.com/paper/2103.14862/) (2021)
- [Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors](https://mlsanity.com/paper/2006.15417/) (2020)
- [Explanations in Autonomous Driving: A Survey](https://mlsanity.com/paper/2103.05154/) (2021)
- [Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1908.02686/) (2019)
- [Towards Better Explanations of Class Activation Mapping](https://mlsanity.com/paper/2102.05228/) (2021)
- [Understanding Individual Decisions of CNNs via Contrastive Backpropagation](https://mlsanity.com/paper/1812.02100/) (2018)
- [U-CAM: Visual Explanation using Uncertainty based Class Activation Maps](https://mlsanity.com/paper/1908.06306/) (2019)
- [Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation](https://mlsanity.com/paper/1908.04351/) (2019)
- [Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling](https://mlsanity.com/paper/1807.06843/) (2018)
- [LFI-CAM: Learning Feature Importance for Better Visual Explanation](https://mlsanity.com/paper/2105.00937/) (2021)
- [Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification](https://mlsanity.com/paper/1809.03851/) (2018)
- [FD-CAM: Improving Faithfulness and Discriminability of Visual Explanation for CNNs](https://mlsanity.com/paper/2206.08792/) (2022)
- [Measuring and improving the quality of visual explanations](https://mlsanity.com/paper/2003.08774/) (2020)

## Built on by (most cited first, 5 of 5)

- [Towards Fine-Grained Interpretability: Counterfactual Explanations for Misclassification with Saliency Partition](https://mlsanity.com/paper/2511.07974/) (2025)
- [Enhancing Explainable AI: A Hybrid Approach Combining GradCAM and LRP for CNN Interpretability](https://mlsanity.com/paper/2405.12175/) (2024)
- [Training with Explanations Alone: A New Paradigm to Prevent Shortcut Learning](https://mlsanity.com/paper/2407.09788/) (2024)
- [Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems](https://mlsanity.com/paper/2412.20201/) (2024)
- [Beyond Output Faithfulness: Learning Attributions that Preserve Computational Pathways](https://mlsanity.com/paper/2509.04588/) (2025)

## Abstract (arXiv)

Recently, the explanation of neural network models has garnered considerable research attention. In computer vision, CAM (Class Activation Map)-based methods and LRP (Layer-wise Relevance Propagation) method are two common explanation methods. However, since most CAM-based methods can only generate global weights, they can only generate coarse-grained explanations at a deep layer. LRP and its variants, on the other hand, can generate fine-grained explanations. But the faithfulness of the explanations is too low. To address these challenges, in this paper, we propose FG-CAM (Fine-Grained CAM), which extends CAM-based methods to enable generating fine-grained and high-faithfulness explanations. FG-CAM uses the relationship between two adjacent layers of feature maps with resolution differences to gradually increase the explanation resolution, while finding the contributing pixels and filtering out the pixels that do not contribute. Our method not only solves the shortcoming of CAM-based methods without changing their characteristics, but also generates fine-grained explanations that have higher faithfulness than LRP and its variants. We also present FG-CAM with denoising, which is a variant of FG-CAM and is able to generate less noisy explanations with almost no change in explanation faithfulness. Experimental results show that the performance of FG-CAM is almost unaffected by the explanation resolution. FG-CAM outperforms existing CAM-based methods significantly in both shallow and intermediate layers, and outperforms LRP and its variants significantly in the input layer. Our code is available at https://github.com/dongmo-qcq/FG-CAM.