Abstract:
Deep learning has recently reshaped the landscape of computer vision research and application. The depth of neural networks is of central importance for recognition accuracy, but deeper neural networks are more difficult to train. In this talk, I will discuss the challenges as well as recent advances of learning deeper networks. I will introduce my recent work called Deep Residual Learning that enables ultra-deep networks with 150+ layers. This method is the foundation of our 1st-place winning entries in all five main tracks in ImageNet and COCO 2015 competitions which cover image classification, object detection, and semantic segmentation. This talk also covers the advance of object detection systems and the intuitions behind them, and highlights the importance of learning visual features for recognition.
Bio: Dr. Kaiming He is a Research Scientist at Facebook AI Research (FAIR) as of August 2016. Before that, he was a Lead Researcher at Microsoft Research Asia (MSRA) which he joined in 2011. His research interests are on computer vision and deep learning. He has received two CVPR Best Paper Awards as the first author, respectively in 2009 and 2016. His work on Deep Residual Networks (ResNets) won the 1st places in all five major tracks in ImageNet and MS COCO competitions 2015 that covered image classification, object detection, and semantic segmentation. He received the PhD degree in 2011 from the Chinese University of Hong Kong, and the BS degree in 2007 from Tsinghua University.