Deep Joint Task Learning for Generic Object Extraction
Sun Yat-sen University（中山大学）；Carnegie Mellon University（卡内基梅隆大学）；SYSU-CMU Shunde International Joint Research Institute（中山大学-卡内基梅隆大学顺德国际联合研究院）；Harbin Institute of Technology（哈尔滨工业大学）
This paper investigates how to extract objects-of-interest without relying on hand-craft features and sliding windows approaches, that aims to jointly solve two sub-tasks: (i) rapidly localizing salient objects from images, and (ii) accurately segmenting the objects based on the localizations. We present a general joint task learning framework, in which each task (either object localization or object segmentation) is tackled via a multi-layer convolutional neural network, and the two networks work collaboratively to boost performance. In particular, we propose to incorporate latent variables bridging the two networks in a joint optimization manner. The first network directly predicts the positions and scales of salient objects from raw images, and the latent variables adjust the object localizations to feed the second network that produces pixelwise object masks. An EM-type method is presented for the optimization, iterating with two steps: (i) by using the two networks, it estimates the latent variables by employing an MCMC-based sampling method; (ii) it optimizes the parameters of the two networks unitedly via back propagation, with the fixed latent variables. Extensive experiments suggest that our framework significantly outperforms other state-of-the-art approaches in both accuracy and efficiency (e.g. 1000 times faster than competing approaches).
Brain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approach
Department of Computer Science, University of North Carolina at Chapel Hill, USA（北卡罗来纳大学教堂山分校计算机科学系）；Department of Computer Science, University of Salzburg, Austria（萨尔茨堡大学计算机科学系）；Kitware Inc., USA（Kitware公司）；Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA（宾夕法尼亚大学佩雷尔曼医学院生物医学图像计算与分析中心）；Department of Computer Science, Technical University of Munich, Germany（慕尼黑工业大学计算机科学系）；Institute of Neuroimaging and Informatics, University of Southern California, USA（南加州大学神经影像与信息学研究所）；David Geffen School of Medicine, UCLA Medical Center, USA（加州大学洛杉矶分校医学中心大卫·格芬医学院）
