Visual concept discovery aims to extract compact and structured representations of the visual world, and recompose them to tackle novel intricate problems. It has played a crucial role in many core problems in computer vision research, including both discriminative and generative tasks. An important research question is to understand and design concept representations that facilitate better learning from various datasets and compositional reasoning. As an endeavor to answering this question, in this workshop, we gather together researchers in computer vision, multi-modal learning, machine learning, and cognitive science to discuss the development and interpretation of visual concept learning systems and their applications.