Visual perception is crucial for a wide range of applications. Traditionally, visual perception models were developed under a closed-world paradigm, where data distributions and categorical labels were assumed to be fixed and known in advance. However, these closed-world models often prove brittle when deployed in the real open world, which is dynamic, vast, and unpredictable. Modern approaches to visual perception have shifted towards open-world models, such as pretraining foundation models on large datasets sourced from the open world (e.g., data collected from the Internet). These foundation models are then adapted to solve specific downstream tasks. While contemporary model training follows the principle of "open-world learning," our workshop seeks to address existing limitations, potential risks, new opportunities, and challenges.