Congratulations to Yeji Shen for Passing His Defense
Congratulations to Yeji Shen for passing his defense on Sep 7, 2021. His Ph.D. thesis is entitled “Labeling Cost Reduction Techniques for Deep Learning: Methodologies and Applications”. Here we invite Yeji to share a brief introduction of his thesis and some words he would like to say at the end of the Ph.D. study journey.
1) Abstract of Thesis
Deep learning has contributed to a significant performance boost of many computer vision tasks. Still, the success of most existing deep learning techniques relies on a large number of labeled data. While data labeling is costly, a natural question arises: is it possible to achieve better performance with the same budget of data labeling? We provide two directions to address the problem: more efficient utilization of the budget or supplementing unlabeled data with no labeling cost. Specifically, in this dissertation, we study three problems related to the topic of reducing the labeling cost: 1) active learning that aims at identifying most informative unlabeled samples for labeling; 2) weakly supervised 3D human pose estimation that utilizes a special type of unlabeled data, action-frozen people videos, to help improve the performance with few manual annotations; and 3) self-supervised representation learning on a large-scale dataset of images with text and user-input tags at no additional labeling cost.
In the first part of this talk, we will introduce our representation work which mainly focuses on the utilization of textual information in images. Text information inside images could provide valuable cues for image understanding. We propose a simple but effective representation learning framework, called the Self-Supervised Representation learning of Images with Texts (SSRIT). SSRIT exploits optical character recognition (OCR) signals in a self-supervision manner. SSRIT constructs a representation that is trained to predict whether [...]