面向计算机视觉模型的人类注意力引导可解释人工智能
Human Attention-Guided Explainable Artificial Intelligence for Computer Vision Models
AI总结：
该研究针对计算机视觉模型的显著性可解释AI方法，提出人类注意力引导的HAG-XAI框架，通过可训练函数与平滑核对齐人类注意力，在目标检测任务上同时提升解释的合理性与忠实度。
摘要：
我们研究了将人类注意力知识嵌入面向计算机视觉模型的基于显著性的可解释人工智能（XAI，explainable AI）方法中，是否能够提升解释的合理性与忠实度。我们首先针对目标检测模型开发了新的基于梯度的XAI方法，通过扩展现有图像分类模型的方法生成特定于目标的解释。有趣的是，尽管这些基于梯度的方法在解释图像分类模型时效果良好，但当用于解释目标检测模型时，执行相同任务的情况下，生成的显著性图的忠实度通常低于人类注意力图。随后我们开发了人类注意力引导XAI（HAG-XAI，Human Attention-Guided XAI），通过可训练的激活函数与平滑核最大化XAI显著性图与人类注意力图的相似度，从人类注意力中学习如何最优整合模型的解释信息以提升解释合理性。对于图像分类模型，HAG-XAI以牺牲忠实度为代价提升了解释合理性；而对于目标检测模型，该方法同时提升了合理性与忠实度，且性能优于现有方法。所学习的函数具有模型特异性，可良好泛化至其他数据库。
英文摘要：
We examined whether embedding human attention knowledge into saliency-based explainable AI (XAI) methods for computer vision models could enhance their plausibility and faithfulness. We first developed new gradient-based XAI methods for object detection models to generate object-specific explanations by extending the current methods for image classification models. Interestingly, while these gradient-based methods worked well for explaining image classification models, when being used for explaining object detection models, the resulting saliency maps generally had lower faithfulness than human attention maps when performing the same task. We then developed Human Attention-Guided XAI (HAG-XAI) to learn from human attention how to best combine explanatory information from the models to enhance explanation plausibility by using trainable activation functions and smoothing kernels to maximize XAI saliency map's similarity to human attention maps. While for image classification models, HAG-XAI enhanced explanation plausibility at the expense of faithfulness, for object detection models it enhanced plausibility and faithfulness simultaneously and outperformed existing methods. The learned functions were model-specific, well generalizable to other databases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
