UoLMM'22: 2nd International Workshop on Robust Understanding of Low-quality Multimedia Data: Unitive Enhancement, Analysis and Evaluation
Liang Liao, Dan Xu, Yang Wu, Xiao Wang, Jing Xiao · Proceedings of the 30th ACM International Conference on Multimedia · 2022
Low-quality multimedia data (including low resolution, low illumination, defects, blurriness, etc.) often pose a challenge for content understanding, as algorithms are typically developed under ideal conditions (high resolution and good visibility). To alleviate this problem, data enhancement techniques (e.g., super-resolution, low-light enhancement, derain, and inpainting) have been proposed to restore low-quality multimedia data. Efforts are also being made to develop robust content understanding algorithms in adverse weather and lighting conditions. Some quality assessment techniques aiming at evaluating the analytical quality of data have also emerged. Even though these topics are mostly studied independently, they are tightly related in terms of ensuring a robust understanding of multimedia content. For example, enhancement should maintain the semantic consistency of the analysis, while quality assessment should consider the comprehensibility of the multimedia data. The purpose of this workshop is to bring together individuals in three areas: enhancement, analysis, and evaluation, for sharing ideas and discussion on current developments and future directions.