Weekly supervised learning from images and video
Dr. Ivan Laptev of INRIA, Paris
Thursday, September 29, 2016 · 11:00AM · HEC 101
Recent progress in visual recognition goes hand-in-hand with the supervised learning and large-scale training data. While the amount of existing images and videos is huge, their detailed annotation is expensive and often ambiguous. To address these problems, in this talk we will focus on weakly-supervised learning methods using incomplete and noisy supervision for training. In the first part I will discuss recognition from still images and will describe our work on weakly-supervised convolutional networks for recognizing and localizing objects and human actions. The second part of the talk will focus on the learning of human actions from videos. In particular we will consider understanding specific tasks from YouTube instruction videos and corresponding narrations. We will conclude with future challenges and opportunities of visual recognition.
Ivan Laptev is a research director at INRIA Paris, France. He received a Habilitation degree from École Normale Supérieure in 2013 and a PhD degree in Computer Science from the Royal Institute of Technology in 2004. Ivan's main research interests include visual recognition of human actions, objects and interactions. He has published over 50 papers at international conferences and journals of computer vision and machine learning. He serves as an associate editor of IJCV and TPAMI journals, he will serve as a program chair for CVPR'18, he was/is an area chair for CVPR'10,'13,'15,'16 ICCV'11, ECCV'12,'14 and ACCV'14,'16 he has co-organized several tutorials, workshops and challenges at major computer vision conferences. He has also co-organized a series of INRIA summer schools on computer vision and machine learning (2010-2013). He received the ERC Starting Grant in 2012.