用草图生成更直观的图像分类解释，提升理解速度与准确性。
SketchXplain: Intuitive Visual Explanations of Image Classifiers with Sketches
- 结合显著性图与概念瓶颈模型，生成语义连贯的草图解释。
- 用户研究显示草图解释比传统热力图更快、更符合直觉。
- 适合医疗等需快速理解场景，尤其利于非专业人士诊断辅助。
显著性图虽能定位图像中影响预测的区域，但往往难以理解且语义模糊，存在可解释性鸿沟。本文提出SketchXplain，通过艺术草图形式生成直观的图像分类解释。该方法融合显著性图、概念瓶颈模型与草图优化技术：利用显著性选择连贯的视觉特征，借助概念保证知识一致性，用线条提示关键信息，并通过抽象化实现简洁表达。在人脸表情识别和皮肤病变诊断任务上的实验表明，相比传统热力图或简单涂鸦，SketchXplain的可视化结果更具可读性，用户解读速度更快且更契合其认知。特别是在皮肤病变诊断中，草图能更一致地呈现病灶特征，有效支持非专业人员的初步判断。本工作验证了草图在实现直观、简洁、一致、快速的图像类XAI可视化中的价值。
原文摘要 · Abstract (English)
Saliency map visualizations explain image-based AI predictions by pointing to regions, but these are often unintuitive and semantically unclear, leaving an interpretability gap. We argue that AI explanations should be intuitive -- coherent to user knowledge, yet simple and selective to accelerate interpretation. Inspired by artistic drawings, we propose SketchXplain to generate sketch-based visual explanations for intuitive image-based explainable AI (XAI). Combining techniques in saliency maps, concept-bottleneck models, and sketch optimization, SketchXplain integrates saliency to select coherent observation artifacts, concepts for knowledge coherence, cues to represent them, and abstraction for simplicity. Evaluating on face expression recognition, modeling and user studies showed that SketchXplain supported quicker interpretation with more aligned visualizations than saliency maps or simple drawings. Further evaluation on skin lesion diagnosis found that SketchXplain more coherently visualized disease symptoms, better supporting lay diagnosis. Thus, this work illustrates the value of sketches for intuitive, simple, coherent, and quick image-based XAI visualizations.
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