Label Efficient Visual Abstractions for Autonomous Driving

This course focuses on exploring label-efficient visual abstractions for autonomous driving. The learning outcomes include understanding the impact of reducing annotation costs on behavior cloning agents and analyzing segmentation-based modalities for self-driving tasks. The course teaches practical insights on exploiting visual abstractions in a label-efficient manner to enhance driving performance. The teaching method involves a keynote presentation with a focus on theoretical concepts and practical applications. The intended audience for this course includes researchers, practitioners, and enthusiasts in the fields of computer vision, autonomous driving, and machine learning.