Weakly supervised object recognition with convolutional neural network
Successful methods for visual object recognition typically rely on large image datasets with rich annotation. Detailed image annotation in terms of object bounding boxes or object parts is both expensive and subjective. In this talk I will present a weakly supervised convolutional neural network (ConvNet) that achieves state-of-the-art results without using detailed annotation. In particular, I will show results for object and action recognition in still images where the network learns to recognize and localize objects and human actions without using location supervision at the training time. We show that our weakly-supervised method achieves comparable performance to its strongly-supervised counterpart.
Ivan Laptev is a research director at INRIA Paris, France. He received 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. He serves as an associate editor for IJCV, TPAMI and IVC journals, he was/is an area chair for CVPR'10,'13,'15, ICCV'11, ECCV'12,'14 and ACCV'14, he has co-organized several tutorials, workshops and challenges. He received ERC Junior Grant in 2012.