Explore a CVPR'20 iMLCV tutorial on modeling conceptual understanding in image reference games presented by Zeynep Akata. Delve into topics such as learning via explanation, attributes and natural language as explanations, grounding visual explanations, and machine theory of mind. Examine the implementation of perceptual modules, agent embeddings, and policy learning in image reference games with failure in concept understanding. Analyze the comparison of learned policies against baselines, evaluate cluster quality, and review qualitative results of modeling conceptual understanding. Gain insights into rational quantitative attribution of beliefs, desires, and percepts in human mentalizing, as well as mind-aware multi-agent management reinforcement learning.