From Segment Anything Efficiently to Matting Anyone Precisely
I will present a unified view of recent advances in efficient segmentation and matting, beginning with EdgeSAM – a compact, prompt-aware variant of Segment Anything that achieves real-time segmentation on mobile devices via prompt-in-the-loop distillation. Building on this, EdgeTAM extends promptable segmentation to video with a spatial perceiver for memory compression and a two-stage distillation pipeline, enabling high-quality tracking at 16 FPS on smartphones. Finally, MatAnyone introduces a memory-based framework for video matting, achieving fine boundary details and temporal consistency through region-adaptive memory fusion and segmentation-guided supervision.
Speaker Biography
Chen Change Loy is a President’s Chair Professor with the College of Computing and Data Science, Nanyang Technological University, Singapore. He is the Lab Director of MMLab@NTU and Co-associate Director of S-Lab. He received his Ph.D. (2010) in Computer Science from the Queen Mary University of London. Prior to joining NTU, he served as a Research Assistant Professor at the MMLab of The Chinese University of Hong Kong, from 2013 to 2018.
His research interests include computer vision and deep learning with a focus on image/video restoration and enhancement, generative tasks, and representation learning. He served/serves as an Associate Editor of the International Journal of Computer Vision (IJCV), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and Computer Vision and Image Understanding (CVIU). He also serves/served as an Area Chair of top conferences such as ICCV, CVPR, ECCV, ICLR and NeurIPS. He will serve as the Program Co-Chair of CVPR 2026 and General Co-Chair of ACCV 2028.
