Invited Speaker

CHEN CHANGE LOY

Harnessing Diffusion Prior for Content Enhancement and Creation

Abstract: This talk delves into the exploration and application of pre-trained diffusion models for content enhancement and creation. By leveraging the abundant image priors and robust generative capability of diffusion models, we innovatively address diverse applications including face restoration, image super-resolution, image colorization, and video-to-video translation. Our work provides a novel approach to content enrichment by harnessing the inherent structure of visual data through the diffusion process. This strategy elucidates the unique potential of utilizing existing models in diverse domains without explicit retraining, thereby reducing computational overheads and enabling efficient adaptability. Through this discussion, we aim to provide insights into the viability of diffusion models as a powerful tool for image and video enhancement tasks, and stimulate further research in exploiting the generative potential of diffusion models.

Bio: Chen Change Loy is a Professor with the School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore. He serves as the Co-Associate Director for S-Lab, NTU and Director of MMLab@NTU. He received his PhD (2010) in Computer Science from the Queen Mary University of London. Before joining NTU, he served as a Research Assistant Professor at the MMLab of the Chinese University of Hong Kong, from 2013 to 2018. He is the recipient of 2019 Nanyang Associate Professorship (Early Career Award) from Nanyang Technological University. He is recognized as one of the 100 most influential scholars in computer vision by AMiner. His research interests include computer vision and deep learning with a focus on image/video restoration, enhancement, and content creation. He serves as an Associate Editor of the CVIU, IJCV and TPAMI. He also serves/served as the Area Chair of top conferences such as CVPR, ICCV, ECCV, NeurIPS and ICLR. He is a senior member of IEEE.

JIAYING LIU


Low-Light Image Enhancement for Intelligent Analytics

Abstract: Low-light enhancement has been a long-standing research problem for decades. The rapid development of deep learning has led to the prosperity of low-light enhancement algorithms. However, it remains unexplored to develop an efficient and concise enhancement paradigm for improving machine vision analysis tasks in smart cities. This work addresses this gap by constructing relevant datasets as well as benchmarks and developing exemplary methods and applications. In detail, at the data end, we construct a dataset for both human and machine visions, evaluate various methods on this dataset, and develop a low-light image enhancement method optimized with downstream face detectors. Then, for the method effort, we propose lightweight methods that adjust illumination distributions to improve the performance of downstream tasks designed for low-light images. These methods do not need to access the labels in low-light conditions and conduct a plug-and-play role to integrate with different downstream tasks, therefore offering high practical values.

Bio: Jiaying Liu received a Ph.D. degree (Hons.) in computer science from Peking University, Beijing China, 2010. She is currently an Associate Professor, Boya Young Fellow with the Wangxuan Institute of Computer Technology, Peking University, China. She has authored more than 100 technical articles in refereed journals and proceedings, and holds 70 granted patents. Her current research interests include multimedia signal processing, compression, and computer vision. She is a senior member of IEEE/CSIG, and a distinguished member of CCF. She was a visiting scholar with the University of Southern California, Los Angeles, California, from 2007 to 2008. She was a visiting researcher with Microsoft Research Asia, in 2015 supported by the Star Track Young Faculties Award.

Dr. Liu has served as a member of Multimedia Systems and Applications Technical Committee (MSA TC), and Visual Signal Processing and Communications Technical Committee (VSPC TC) in IEEE Circuits and Systems Society. She received the IEEE ICME 2020 Best Paper Award and IEEE MMSP 2015 Top10% Paper Award. She has also served as the Associate Editor of the IEEE Trans. on Image Processing, the IEEE Trans. on Circuits Systems for Video Technology and Journal of Visual Communication and Image Representation, the Technical Program Chair of ACM MM Asia-2023/IEEE ICME-2021/ACM ICMR-2021/IEEE VCIP-2019, the Area Chair of CVPR-2021/ECCV-2020/ICCV-2019, ACM ICMR Steering Committee member and the CAS Representative at the ICME Steering Committee. She was the APSIPA Distinguished Lecturer (2016-2017).

CHEE SENG CHAN


Unveiling the Shadows: Exploration and Introduction of Low Light Environments and Datasets

Abstract: This talk presents the complexities of low light environments and introduces new datasets designed to address existing limitations. With a focus on understanding the challenges posed by reduced illumination, this talks aims to catalyze advancements in computer vision and image processing. The curated dataset reflects diverse real-world scenarios, providing a valuable resource for benchmarking and improving model performance. By shedding light on the nuances of low light conditions, this talk hopes to encourage to the development of more robust algorithms and technologies capable of thriving in challenging lighting environments.

Bio: Dr. Chan is a full Professor at Universiti Malaya (UM), Kuala Lumpur, Malaysia, where he leads a dynamic and enthusiastic research team specializing in computer vision and machine learning. Together with his team, he has published more than 100 papers in top peer-review computer vision/machine learning conferences and journals (e.g. CVPR, NeurIPS, TPAMI, TIP etc).

He was the recipient of Top Research Scientists Malaysia (TRSM) in 2022, Young Scientists Network Academy of Sciences Malaysia (YSN-ASM) in 2015 and Hitachi Research Fellowship in 2013. Besides that, he is also a senior member of IEEE, Professional Engineer (BEM) and Chartered Engineer (IET).

From 2020 to 2022, he was seconded to the Ministry of Science, Technology and Innovation (MOSTI) as the Lead of PICC Unit under COVID19 Immunisation Task Force (CITF), as well as the Undersecretary for Division of Data Strategic and Foresight.