Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks

SCAAT：通过显著性约束的自适应对抗训练提升神经网络可解释性

SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training

本文提出SCAAT方法，利用显著性图引导构造对抗样本进行训练，以消除噪声并生成更稀疏、更忠实的显著性图，从而在不改变模型架构和预测性能的前提下提升深度神经网络的可解释性。

利用激活实现超像素解释

Leveraging Activations for Superpixel Explanations

本文提出神经激活超像素（NAS），无需微调网络即可从分类器激活中提取分割，提升弱监督定位性能，并支持显著性方法的半监督语义评估与指标一致性分析。

REVEX：视频中基于移除的可解释人工智能统一框架

REVEX: A Unified Framework for Removal-Based Explainable Artificial Intelligence in Video

本文提出基于移除的视频解释框架REVEX，通过添加时间信息和局部解释将六种现有技术适配到视频中，全面评估发现Video LIME和Video RISE表现较优，并揭示了XAI评估指标的局限性与适用性。

Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification

Explaining decisions of black-box classifiers is paramount in sensitive domains such as medical imaging since clinicians confidence is necessary for adoption. Various explanation approaches have been proposed, among which perturbation based approaches are very promising. Within this class of methods, we leverage a learning framework to produce our visual explanations method. From a given classifier, we train two generators to produce from an input image the so called similar and adversarial images. The similar image shall be classified as the input image whereas the adversarial shall not. Visu…

"Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution

Understanding the decision process of neural networks is hard. One vital method for explanation is to attribute its decision to pivotal features. Although many algorithms are proposed, most of them solely improve the faithfulness to the model. However, the real environment contains many random noises, which may leads to great fluctuations in the explanations. More seriously, recent works show that explanation algorithms are vulnerable to adversarial attacks. All of these make the explanation hard to trust in real scenarios. To bridge this gap, we propose a model-agnostic method \emph{Median Te…

Diffusion Visual Counterfactual Explanations

Visual Counterfactual Explanations (VCEs) are an important tool to understand the decisions of an image classifier. They are 'small' but 'realistic' semantic changes of the image changing the classifier decision. Current approaches for the generation of VCEs are restricted to adversarially robust models and often contain non-realistic artefacts, or are limited to image classification problems with few classes. In this paper, we overcome this by generating Diffusion Visual Counterfactual Explanations (DVCEs) for arbitrary ImageNet classifiers via a diffusion process. Two modifications to the di…

Toward Affective XAI: Facial Affect Analysis for Understanding Explainable Human-AI Interactions

As machine learning approaches are increasingly used to augment human decision-making, eXplainable Artificial Intelligence (XAI) research has explored methods for communicating system behavior to humans. However, these approaches often fail to account for the emotional responses of humans as they interact with explanations. Facial affect analysis, which examines human facial expressions of emotions, is one promising lens for understanding how users engage with explanations. Therefore, in this work, we aim to (1) identify which facial affect features are pronounced when people interact with XAI…

ProtoVQA：一种面向可解释细粒度视觉问答的自适应原型框架

ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering

ProtoVQA提出统一原型框架，通过学习问题感知原型和空间约束匹配，同时支持VQA回答与证据定位，并引入VLAS评估解释质量，在Visual7W上实现可解释性与准确率的兼顾。

PhysNav-DG：一种面向导航应用中鲁棒VLM-传感器融合的新型自适应框架

PhysNav-DG: A Novel Adaptive Framework for Robust VLM-Sensor Fusion in Navigation Applications

该研究提出PhysNav-DG双分支自适应框架，融合经典传感器滤波与VLM语义能力，同步输出导航动作与思维链解释，还构建多领域基准MD-NEX，实验显示其导航成功率提升超20%，推动自主系统更安全可信。