2D and 3D visual understanding with limited supervision - Wu, Zhonghua (Nanyang Technological University, 2023)
Existing fully supervised deep learning methods usually require a large number of training samples with abundant annotations for the model training, which is extremely expensive and labor-consuming. Therefore, in order to alleviate huge labeling costs, it is highly desirable to develop weakly supervised learning methods. Here, weakly supervised learning refers to that during the model training, the labels of the training data could be inexact, incomplete, or inaccurate. Typically, there are three types of weakly supervised learning scenarios: inexact supervision, incomplete supervision, and inaccurate supervision. In this thesis, we study all three types of weak supervision scenarios based on three different fundamental 2D and 3D recognition tasks including weakly supervised object detection (WSOD), few-shot image segmentation (FSS), and weakly supervised point cloud segmentation (WSPCS).