<p>With the development of computer vision techniques, significant breakthroughs have been achieved in closed-set visual recognition tasks. However, in real-world recognition or classification scenarios, it is often challenging to exhaustively collect training examples for all classes due to various constraints. A more realistic scenario is Visual Open-Set Recognition (OSR), where incomplete knowledge exists during model training, and unknown classes may be encountered during testing. This requires a classifier that can accurately categorize known classes and efficiently handle unknown classes. In this paper, we systematically track and summarize the latest research on visual open-set recognition, providing a comprehensive classification and review of current OSR methods, including DNN-based OSR methods and visual language model guided OSR methods. We then present representative approaches for OSR extension tasks and expansion tasks in open-set environments. Subsequently, we analyze and compare the performance of typical and state-of-the-art OSR methods across different datasets. Finally, we discuss some of the remaining challenges and future research directions in the field of visual open-set recognition.</p>