More about HKUST
A Survey of Multi-Task Learning For Dense Scene Understanding
PhD Qualifying Examination Title: "A Survey of Multi-Task Learning For Dense Scene Understanding" by Mr. Hanrong YE Abstract: Multi-Task Dense Scene Understanding is a critical research topic in the field of computer vision, which has numerous applications in real-world scenarios. To achieve a comprehensive understanding of visual information at the pixel level, several fundamental dense prediction tasks, such as semantic segmentation, depth estimation, and surface normal estimation, are required. Simultaneously learning multiple challenging tasks in a unified model is highly demanding, making multi-task dense scene understanding a challenging problem. In this survey, we aim to provide a systematic review of the developments in this field, particularly from the perspective of deep learning. We begin by defining the problem and highlighting its importance. We then discuss various existing methods that have been proposed to tackle this problem by improving the multi-task optimization process and designing multi-task network architecture. We examine representative works of each genre from different perspectives. Furthermore, we consider a more practical setting: multi-task semi-supervised learning, where training samples are labeled for only some tasks rather than all tasks. We introduce pioneering works in this direction and analyze their advantages and disadvantages. Overall, we hope that this survey will serve as a foundation for further research in this field and inspire new ideas for tackling this challenging problem. Date: Thursday, 20 April 2023 Time: 10:00am - 12:00noon Venue: Room 5501 Lifts 25/26 Committee Members: Dr. Dan Xu (Supervisor) Dr. Qifeng Chen (Chairperson) Prof. Huamin Qu Prof. Raymond Wong **** ALL are Welcome ****