[CFAR Distinguished Professor Lecture Series]Towards Green AI: Efficient Transformers for Visual Recognition (Hybrid Event) by Professor Cai Jianfei
23 Jun 2022 | 10.00am (Singapore Time)
The tremendous success of deep learning is attributed to the vast computational resources available and large amounts of labeled data. Despite its huge excitement, deep learning models have become formidably large and computationally intensive, thus resulting in considerable energy and financial costs. Towards the energy-efficient green AI goal, this talk focuses on the efficiency issues of the current dominant network architecture – Transformers. Although with excellent performance, the transformer is notorious for its costly training and inference due to its quadratic complexity of self-attention over a long sequence of representations, especially for high-resolution dense prediction. Prof Cai Jianfei will introduce a few simple yet effective solutions that can greatly improve the Transformer efficiency without causing a significant drop in performance.
SPEAKER
Cai Jianfei is a Professor at Faculty of IT, Monash University, where he currently serves as the Head for the Data Science & AI Department. He is also a visiting professor at Nanyang Technological University (NTU). Before that, he was Head of Visual and Interactive Computing Division and Head of Computer Communications Division in NTU. His major research interests include computer vision, deep learning and multimedia. He has successfully trained 30+ PhD students with two getting NTU SCSE Outstanding PhD thesis award and one runner-up award. Many of his PhD students joined leading IT companies such as Facebook, Apple, Amazon, NVIDIA and Adobe or become faculty members in reputable universities. He is a co-recipient of paper awards in ACCV, ICCM, IEEE ICIP and MMSP. He serves or has served as an Associate Editor for IJCV, IEEE T-IP, T-MM, and T-CSVT as well as serving as Area Chair for CVPR, ICCV, ECCV, IJCAI, ACM Multimedia, ICME and ICIP. He was the Chair of IEEE CAS VSPC-TC during 2016-2018. He also served as the leading TPC Chair for IEEE ICME 2012 and the best paper award committee chair & co-chair for IEEE T-MM 2020 & 2019. He will be the leading general chair for ACM Multimedia 2024. He is also a Fellow of IEEE.