xAI-CV: An Overview of Explainable Artificial Intelligence in Computer Vision
It tackles the problem of interpretability in AI for researchers and practitioners in critical applications, but is incremental as it is a survey paper.
This paper surveys explainable AI (xAI) methods in computer vision to address the 'black-box' nature of deep learning models, analyzing four representative approaches to provide a comprehensive overview for guiding future research.
Deep learning has become the de facto standard and dominant paradigm in image analysis tasks, achieving state-of-the-art performance. However, this approach often results in "black-box" models, whose decision-making processes are difficult to interpret, raising concerns about reliability in critical applications. To address this challenge and provide human a method to understand how AI model process and make decision, the field of xAI has emerged. This paper surveys four representative approaches in xAI for visual perception tasks: (i) Saliency Maps, (ii) Concept Bottleneck Models (CBM), (iii) Prototype-based methods, and (iv) Hybrid approaches. We analyze their underlying mechanisms, strengths and limitations, as well as evaluation metrics, thereby providing a comprehensive overview to guide future research and applications.