| 报告时间：2016年4月20日（周三）北京时间晚20：40报告摘要：深度学习的有效性已经在机器视觉和模式识别各个领域。本次Webinar中，我们以人体姿态识别(pose estimation)为主要应用背景，介绍我们在利用领域知识（Domain Knowledge）来设计深度学习模型的探索。在此探索中，我们利用深度模型学习两种关系：1.人身体部位之间的关系；2.人体特征表示之间的关系。领域知识为这些关系的建模提供理论指导。我们利用深度学习模型实现关系和特征的联合学习，实现输入端（图片）到输出端（人体部位在图片的位置）之间端到端的深度学习模型。1. Wei Yang Wanli Ouyang Hongsheng Li Xiaogang Wang, End-to-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation, CVPR 2016 (Oral)2. Xiao Chu Wanli Ouyang Hongsheng Li Xiaogang Wang, Structured Feature Learning for Pose Estimation, CVPR 2016 (Spotlight)3. Wanli Ouyang, Xiaogang Wang,Cong Zhang, Xiaokang Yang. Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution, CVPR 2016 (Spotlight)报告人简介：Wanli Ouyang received the PhD degree in the Department of Electronic Engineering, The Chinese University of Hong Kong, where he is now a research assistant professor. His research interests include image processing, computer vision and pattern recognition. ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is one of the most important grand challenges in computer vision. The team led by him ranks No. 2 in the ILSVRC 2014 and 2015 for object detection from image and No. 1 in ILSVRC 2015 for object detection from video. He receives the best reviewer award of ICCV2015. He has been the reviewer of many top journals and conferences such as IEEE TPAMI, TIP, IJCV, TCAS, TSP, TITS, TMM, TNN, TKDE, CVPR, ICCV. He is a member of the IEEE. |
