5月4日计算机学院澳大利亚阿德莱德大学沈春华教授学术讲座预告

    主讲人：沈春华 教授（澳大利亚阿德莱德大学）
    时 间：2015年5月4日(周一)下午15:30
    地 点：屏峰校区广B106
    题 目: Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
    讲座摘要:
    We tackle the problem of depth estimation from single monocular images.Compared with depth estimation using multiple images such as stereo depth perception, depth from monocular images is much bbin电子 challenging. Prior work typically focuses on exploiting geometric priors or additional sources of information, most using hand-crafted features. Recently, there is mounting evidence that features from deep convolutional neural networks (CNN) set new records for various vision applications. On the other hand, considering the continuous characteristic of the depth values, depth estimations can be naturally formulated as a continuous conditional random field (CRF) learning problem. Therefore, here we present a deep convolutional neural field model for estimating depths from single monocular images, aiming to jointly explore the capacity of deep CNN and continuous CRF. In particular, we propose a deep structured learning scheme which learns the unary and pairwise potentials of continuous CRF in a unified deep CNN framework. We then further propose an equally effective model based on fully convolutional networks and a novel superpixel pooling method, which is ～10 times faster, to speedup the patch-wise convolutions in the deep model. With this bbin电子 efficient model, we are able to design deeper networks to pursue better performance. Experiments on both indoor and outdoor scene datasets demonstrate that the proposed method outperforms state-of-the-art depth estimation approaches.
    主讲人简介:
    Chunhua Shen is a Professor at School of Computer Science, University of Adelaide. He is a Project Leader and Chief Investigator at the Australian Research Council Centre of Excellence for Robotic Vision (ACRV), for which he leads the project on machine learning for robotic vision. Before he moved to Adelaide as a Senior Lecturer, he was with the computer vision program at NICTA (National ICT Australia), Canberra Research Laboratory for about six years. His research interests are in the intersection of computer vision and statistical machine learning. Recent work has been on real-time object detection, large-scale image retrieval and classification, and scalable nonlinear optimization. He studied at Nanjing University, at Australian National University, and received his PhD degree from the University of Adelaide. From 2012 to 2016, he holds an Australian Research Council Future Fellowship. He is serving as Associate Editor of IEEE Transactions on Neural Networks and Learning Systems.