# Learning Visual Explanations for DCNN-Based Image Classifiers Using an Attention Mechanism

arXiv: https://arxiv.org/abs/2209.11189 · HTML: https://mlsanity.com/paper/2209.11189/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [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)
- [Multimodal Few-Shot Learning with Frozen Language Models](https://mlsanity.com/paper/2106.13884/) (2021)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Real Time Image Saliency for Black Box Classifiers](https://mlsanity.com/paper/1705.07857/) (2017)
- [Textual Explanations for Self-Driving Vehicles](https://mlsanity.com/paper/1807.11546/) (2018)
- [InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics](https://mlsanity.com/paper/2004.06592/) (2020)
- [Contrastive Explanations in Neural Networks](https://mlsanity.com/paper/2008.00178/) (2020)
- [Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring](https://mlsanity.com/paper/2102.07805/) (2021)
- [Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation](https://mlsanity.com/paper/2010.00672/) (2020)
- [Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks](https://mlsanity.com/paper/2102.07799/) (2021)
- [Why do These Match? Explaining the Behavior of Image Similarity Models](https://mlsanity.com/paper/1905.10797/) (2019)
- [Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels](https://mlsanity.com/paper/2010.08644/) (2020)
- [iCaps: An Interpretable Classifier via Disentangled Capsule Networks](https://mlsanity.com/paper/2008.08756/) (2020)
- [SIDU: Similarity Difference and Uniqueness Method for Explainable AI](https://mlsanity.com/paper/2006.03122/) (2020)

## Built on by (most cited first, 7 of 7)

- [Explain Any Concept: Segment Anything Meets Concept-Based Explanation](https://mlsanity.com/paper/2305.10289/) (2023)
- [Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning](https://mlsanity.com/paper/2509.06461/) (2025)
- [TAME: Attention Mechanism Based Feature Fusion for Generating Explanation Maps of Convolutional Neural Networks](https://mlsanity.com/paper/2301.07407/) (2023)
- [T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers](https://mlsanity.com/paper/2403.04523/) (2024)
- [Deep spatial context: when attention-based models meet spatial regression](https://mlsanity.com/paper/2401.10044/) (2024)
- [An Integrated Framework for Multi-Granular Explanation of Video Summarization](https://mlsanity.com/paper/2405.10082/) (2024)
- [DXAI: Explaining Classification by Image Decomposition](https://mlsanity.com/paper/2401.00320/) (2023)

## Abstract (arXiv)

In this paper two new learning-based eXplainable AI (XAI) methods for deep convolutional neural network (DCNN) image classifiers, called L-CAM-Fm and L-CAM-Img, are proposed. Both methods use an attention mechanism that is inserted in the original (frozen) DCNN and is trained to derive class activation maps (CAMs) from the last convolutional layer's feature maps. During training, CAMs are applied to the feature maps (L-CAM-Fm) or the input image (L-CAM-Img) forcing the attention mechanism to learn the image regions explaining the DCNN's outcome. Experimental evaluation on ImageNet shows that the proposed methods achieve competitive results while requiring a single forward pass at the inference stage. Moreover, based on the derived explanations a comprehensive qualitative analysis is performed providing valuable insight for understanding the reasons behind classification errors, including possible dataset biases affecting the trained classifier.