Learning to Perceive and Model the World at Scale for Autonomous AI
Speaker:
Mr. XIONG Yuwen
Ph.D. Candidate
Department of Computer Science, University of Toronto
Abstract:
Developing truly autonomous AI systems like self-driving cars has the potential to transform various industries and improve our daily lives. Accomplishing such a system hinges on two crucial components. First, precise perception of the world is necessary; second, modeling and predicting the world’s dynamics is essential to interact with the real world effectively.
In this talk, I will outline my research efforts in perception and world modeling, focusing on developing scalable deep learning algorithms and models beyond controlled environments.
Regarding perception, I will delve into the development of core deep-learning operators that fundamentally augment the capabilities of deep learning models, followed by discussions on how to perform unsupervised pretraining design unified neural network architectures for efficient and effective image segmentation.
As for world modeling, I will show how to learn prior knowledge of the world and then learn to accurately predict world dynamics at the observational level, both in a scalable and unsupervised manner. Lastly, I will discuss my future research plans to advance perception and world modeling further. This involves integrating multi-modal information into the models and systematically incorporating external knowledge, which is crucial for realizing intelligent autonomous AI systems.
Biography:
Yuwen Xiong is a Ph.D. candidate at the University of Toronto, advised by Professor Raquel Urtasun. He was a research scientist in industrial labs (e.g., Uber ATG, Waabi), conducting cutting-edge research on autonomous driving. Before coming to Toronto, he collaborated with Dr Jifeng Dai at Microsoft Research Asia. Yuwen Xiong’s primary interest lies at the intersection of computer vision, robotics, and machine learning. His long-term vision is to build autonomous AI systems that can learn like humans and operate reliably in the real world. To this end, he leverages his knowledge in the full spectrum of autonomy, including perception, prediction, decision-making, and 3D generation, to create systems that are flexible to handle real-world complexities, robust to uncertainties, and generalizable to novel scenes. He is a recipient of the Canada Graduate Scholarships – Doctoral and the Borealis AI Fellowship. More information about him can be found at https://www.cs.toronto.edu/~yuwen/.
Enquiries: WONG O-Bong (obong@cse.cuhk.edu.hk)