Advancing the theoretical foundations and practical applications of computer vision and machine learning for embodied agents.
Our research addresses the theoretical foundations and practical applications of computer vision and machine learning for an embodied AI to perceive, predict and interact with the dynamic environment around it.
Our interest lies in discovering and proposing the fundamental principles, algorithms and practical implementations for solving high-level visual perception such as:
Our overarching aim is to develop an end-to-end perception system for an embodied agent to learn, perceive and act simultaneously through interaction with the dynamic world.
Latest Project
CVPR 2019
Congratulations to Zhixi and the team for this outstanding achievement.
Congratulations to Saikat for his work on scale-aware segmentation.
Tho Le's work on generalized intersection over union for parametric shapes.