Title: Learning and Adapting from the Web for Visual Recognition (Slides)
Abstract: Visual recognition is of fundamental importance to computer vision and downstream applications. Substantial research in the last half decade shows that coupling powerful deep learning models with a big manually compiled dataset can yield excellent results for generic object, scene, and human activity recognition. However, it is both costly and laborious to select and label data, not mentioning that the data is extremely scarce in some domains. As a result, transfer learning, semi-supervised learning, self-supervised learning, and webly-supervised learning, etc. have gained increasing interests recently, thanks to their potential of leveraging the deep models yet with low-cost data. In this talk, I will progressively present three of our recent works on learning and adapting from the Web data for visual recognition. In the first setting, we show how to effectively learn from Google Images by an outlier-resilient semi-supervised learning method. In the second setting, we investigate surprisingly accurate geometric labels encoded by the 3D videos/movies which are vastly available on the Web. Finally, we let the Web images and Web videos mutually vote for the query-relevant subsets in order to automatically harvest a webly labeled diverse training set for human activity recognition. In parallel with the efforts of exploiting the Web data, I will also present in-depth some of the techniques behind those works (e.g., the multiple shades of dropout, curriculum learning, and kernel mean matching), shedding lights on the variations of them in our other works on WGAN and domain adaptation.
Biography: Dr. Boqing Gong is a Principal Researcher of Tencent AI Lab at Seattle, working on machine learning and computer vision. Before joining Tencent, he was a tenure-track Assistant Professor in University of Central Florida (UCF). His research in UCF was supported in part by an NSF CRII award (so-PI, received in 2016) and an NSF BIGDATA award (PI, received in 2017), both of which were the first of their kinds ever granted to UCF. He actively serves on NSF panels and the program committees of computer vision conferences (CVPR, ICCV, ECCV, etc.) and machine learning conferences (ICML, NIPS, AISTATS, etc.). He was an area chair of IEEE WACV'18 and a mentor of its PhD forum. In 2015, he received a Ph.D. degree in Computer Science from the University of Southern California, where his work was partially supported by the Viterbi Fellowship.