Human-Centric AI for Computer Vision and Machine Autonomy - ICCV'21 Tutorial
Explore a comprehensive lecture from ICCV'21 Tutorial on human-centric AI for computer vision and machine autonomy. Delve into the challenges and successes of AI, examining whether deep AI models can think like humans. Investigate explainable AI techniques such as Class Activation Mapping (CAM) for scene classification and medical imaging. Learn about network dissection, rapid progress in image generation, and identifying causality in latent space. Discover how to steer generative models and understand the role of individual units. Examine human-in-the-loop reinforcement learning and its impact on improving environment diversity and generalizability. Gain insights into the key impact areas of human-centric AI in this informative 39-minute presentation by Bolei Zhou.