Visual recognition models have achieved unprecedented success in various tasks. While researchers aim to understand the underlying mechanisms of these models, the growing demand for deployment in safety-critical areas like autonomous driving and medical diagnostics has accelerated the development of eXplainable AI (XAI). Distinct from generic XAI, visual recognition XAI is positioned at the intersection of vision and language, which represent the two most fundamental human modalities and form the cornerstones of multimodal intelligence. This paper provides a systematic survey of XAI in visual recognition by establishing a multi-dimensional taxonomy from a human-centered perspective based on intent, object, presentation, and methodology. Beyond categorization, we summarize critical evaluation desiderata and metrics, conducting an extensive qualitative assessment across different categories and demonstrating quantitative benchmarks within specific dimensions. Furthermore, we explore the interpretability of Multimodal Large Language Models and practical applications, identifying emerging trends and opportunities. By synthesizing these diverse perspectives, this survey provides an insightful roadmap to inspire future research on the interpretability of visual recognition models.

People can usually give reasons for recognizing a particular object as a specific category, using various means such as body language (by pointing out) and natural language (by telling). This inspires us to develop a recognition model with such principles to explain the recognition process to enhance human trust. We propose Semantic Prototype Analysis Network (SPANet), an interpretable object recognition approach that enables models to explicate the decision process more lucidly and comprehensibly to humans by "pointing out where to focus" and "telling about why it is" simultaneously. With the proposed method, some part prototypes with semantic concepts will be provided to elaborate on the classification together with a group of visualized samples to achieve both part-wise and semantic interpretability. The results of extensive experiments demonstrate that SPANet is able to recognize objects almost as well as the non-interpretable models, at the same time generating intelligible explanations for its decision process.