Weining Lu

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In this paper, we attack the anomaly detection problem by directly modeling the data distribution with deep architectures. We propose deep structured energy based models (DSEBMs), where the energy function is the output of a de-terministic deep neural network with structure. We develop novel model architectures to integrate EBMs with different types of data(More)
Building large models with parameter sharing accounts for most of the success of deep convolutional neural networks (CNNs). In this paper, we propose doubly con-volutional neural networks (DCNNs), which significantly improve the performance of CNNs by further exploring this idea. In stead of allocating a set of convolutional filters that are independently(More)
Pose estimation from points with unknown correspondences currently is still a difficult problem in the field of computer vision. To solve this problem, the SoftSI algorithm is proposed, which can simultaneously obtain pose and correspondences. The SoftSI algorithm is based on the combination of the proposed PnP algorithm (the SI algorithm) and two singular(More)
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