# RES: A Robust Framework for Guiding Visual Explanation

arXiv: https://arxiv.org/abs/2206.13413 · HTML: https://mlsanity.com/paper/2206.13413/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 45 references that are in the map.
- First posted on arXiv 27 Jun 2022.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [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)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017)
- [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://mlsanity.com/paper/1707.09457/) (2017)
- [Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations](https://mlsanity.com/paper/1703.03717/) (2017)
- [Women also Snowboard: Overcoming Bias in Captioning Models](https://mlsanity.com/paper/1803.09797/) (2018)
- [Local Rule-Based Explanations of Black Box Decision Systems](https://mlsanity.com/paper/1805.10820/) (2018)
- [Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?](https://mlsanity.com/paper/1606.03556/) (2016)
- [Attention Branch Network: Learning of Attention Mechanism for Visual Explanation](https://mlsanity.com/paper/1812.10025/) (2018)
- [Aligning Faithful Interpretations with their Social Attribution](https://mlsanity.com/paper/2006.01067/) (2020)
- [Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs](https://mlsanity.com/paper/2004.11440/) (2020)
- [Exploring Human-like Attention Supervision in Visual Question Answering](https://mlsanity.com/paper/1709.06308/) (2017)
- [Embedding Human Knowledge into Deep Neural Network via Attention Map](https://mlsanity.com/paper/1905.03540/) (2019)
- [Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining](https://mlsanity.com/paper/1808.00265/) (2018)
- [AiR: Attention with Reasoning Capability](https://mlsanity.com/paper/2007.14419/) (2020)
- [Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment](https://mlsanity.com/paper/2202.02838/) (2022)
- [Interpreting and Evaluating Neural Network Robustness](https://mlsanity.com/paper/1905.04270/) (2019)
- [Explanation vs Attention: A Two-Player Game to Obtain Attention for VQA](https://mlsanity.com/paper/1911.08618/) (2019)
- [VisualBackProp: efficient visualization of CNNs](https://mlsanity.com/paper/1611.05418/) (2016)
- [BEAN: Interpretable Representation Learning with Biologically-Enhanced Artificial Neuronal Assembly Regularization](https://mlsanity.com/paper/1909.13698/) (2019)
- [Few-Shot Learning with Per-Sample Rich Supervision](https://mlsanity.com/paper/1906.03859/) (2019)

## Built on by (most cited first, 22 of 22)

- [Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning](https://mlsanity.com/paper/2212.03954/) (2022)
- [Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations](https://mlsanity.com/paper/2212.03840/) (2022)
- [Studying How to Efficiently and Effectively Guide Models with Explanations](https://mlsanity.com/paper/2303.11932/) (2023)
- [Good Teachers Explain: Explanation-Enhanced Knowledge Distillation](https://mlsanity.com/paper/2402.03119/) (2024)
- [B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable](https://mlsanity.com/paper/2411.00715/) (2024)
- [A survey on Concept-based Approaches For Model Improvement](https://mlsanity.com/paper/2403.14566/) (2024)
- [Saliency-Bench: A Comprehensive Benchmark for Evaluating Visual Explanations](https://mlsanity.com/paper/2310.08537/) (2023)
- [Saliency-Regularized Deep Multi-Task Learning](https://mlsanity.com/paper/2207.01117/) (2022)
- [Saliency-Guided Hidden Associative Replay for Continual Learning](https://mlsanity.com/paper/2310.04334/) (2023)
- [DUE: Dynamic Uncertainty-Aware Explanation Supervision via 3D Imputation](https://mlsanity.com/paper/2403.10831/) (2024)
- [Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations](https://mlsanity.com/paper/2307.04036/) (2023)
- [On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical Imaging](https://mlsanity.com/paper/2505.10231/) (2025)
- [Visual Attention Prompted Prediction and Learning](https://mlsanity.com/paper/2310.08420/) (2023)
- [FastRM: An efficient and automatic explainability framework for multimodal generative models](https://mlsanity.com/paper/2412.01487/) (2024)
- [AIM: Amending Inherent Interpretability via Self-Supervised Masking](https://mlsanity.com/paper/2508.11502/) (2025)
- [Large Language Models as Attribution Regularizers for Efficient Model Training](https://mlsanity.com/paper/2502.20268/) (2025)
- [3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration](https://mlsanity.com/paper/2401.15877/) (2024)
- [Effective Guidance for Model Attention with Simple Yes-no Annotations](https://mlsanity.com/paper/2410.22312/) (2024)
- [Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment](https://mlsanity.com/paper/2506.07168/) (2025)
- [From Attribution to Action: Jointly ALIGNing Predictions and Explanations](https://mlsanity.com/paper/2511.06944/) (2025)
- [Hybrid Attribution Priors for Explainable and Robust Model Training](https://mlsanity.com/paper/2512.14719/) (2025)
- [Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence](https://mlsanity.com/paper/2607.08641/) (2026)

## Abstract (arXiv)

Despite the fast progress of explanation techniques in modern Deep Neural Networks (DNNs) where the main focus is handling "how to generate the explanations", advanced research questions that examine the quality of the explanation itself (e.g., "whether the explanations are accurate") and improve the explanation quality (e.g., "how to adjust the model to generate more accurate explanations when explanations are inaccurate") are still relatively under-explored. To guide the model toward better explanations, techniques in explanation supervision - which add supervision signals on the model explanation - have started to show promising effects on improving both the generalizability as and intrinsic interpretability of Deep Neural Networks. However, the research on supervising explanations, especially in vision-based applications represented through saliency maps, is in its early stage due to several inherent challenges: 1) inaccuracy of the human explanation annotation boundary, 2) incompleteness of the human explanation annotation region, and 3) inconsistency of the data distribution between human annotation and model explanation maps. To address the challenges, we propose a generic RES framework for guiding visual explanation by developing a novel objective that handles inaccurate boundary, incomplete region, and inconsistent distribution of human annotations, with a theoretical justification on model generalizability. Extensive experiments on two real-world image datasets demonstrate the effectiveness of the proposed framework on enhancing both the reasonability of the explanation and the performance of the backbone DNNs model.