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讲座题目:An Introduction to Deep Learning in Computer Vision
内容简介：In this talk, I give an overview of milestone convolutional neural networks in computer vision since 2012, and divide them into two categories: one from image classification, including AlexNet, GoogleNet, VGGNet, ResNet, and DenseNet, and the other one for universal tasks, HRNet. I mainly introduce HRNet that was developed by my team. I show that HRNet empirically outperforms the previous standard ResNet for almost all visual recognition tasks, such as semantic segmentation, object detection, human pose estimation, face alignment, and so on.
Jingdong Wang is a Senior Principal Research Manager with the Visual Computing Group, Microsoft Research, Beijing, China. He received the B.Eng. and M.Eng. degrees from the Department of Automation, Tsinghua University, Beijing, China, in 2001 and 2004, respectively, and the PhD degree from the Department of Computer Science and Engineering, the Hong Kong University of Science and Technology, Hong Kong, in 2007. His areas of interest include deep learning, large-scale indexing, human understanding, and person re-identification. He is an Associate Editor of IEEE TPAMI, IEEE TMM and IEEE TCSVT, and is an area chair (or SPC) of some prestigious conferences, such as CVPR, ICCV, ECCV, ACM MM, IJCAI, and AAAI. He is a Fellow of IAPR and an ACM Distinguished Member.