The rapid advancement of artificial intelligence (AI) techniques, propelled by foundation models like LLM, has ignited transf ormative revolutions. However, unlike language, foundation models in other modalities are trailing behind, presenting hurdles in expanding AI systems to diverse唧lications. Vision, unlike language, comprises natural signals captured from the environment, encompassing diverse representations such as 3D structures like point clouds and meshes, as well as 2D images and videos. Consequently, vision involves a more intricate and redundant representation, making the development of a foundational model within the vision community a formidable task. This seminar aims to explore vision systems through the lens of self-supervised representation learning, a cornerstone in many foundation models including LLM. The talk will assess existing challenges in mainstream vision self-supervised learning methods, propose feasible solutions, and delve into promising directions for further investigation. Additionally, this seminar will discuss future endeavors in developing versatile representations across modalities, tasks, and architectures, which can propel the evolution of the vision foundation model.

Speaker: Dr Tong ZHANG
Date: 2 May 2024 (Thusday)
Time: 3:00pm – 4:00pm
Poster: Click here