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In this work, we address the challenging video scene parsing problem by developing effective representation learning methods given limited parsing annotations. In particular , we contribute two novel methods that constitute a unified parsing framework. (1) Predictive feature learning from nearly unlimited unlabeled video data. Different from existing(More)
  • Hanlin Tan, Huaxin Xiao, Yu Liu, Maojun Zhang, Bin Wang
  • 2017
Sparse learning based methods are effective for image restoration applications since they make use of texture priors learned by pre-trained over-complete dictionaries. However, sparse learning based methods are extremely slow due to complexity of sparse decomposition and a large number of image patches to process. In this paper, we introduce a fast(More)
In this work, we present a simple, highly efficient and modularized Dual Path Network (DPN) for image classification which presents a new topology of connection paths internally. By revealing the equivalence of the state-of-the-art Residual Network (ResNet) and Densely Convolutional Network (DenseNet) within the HORNN framework, we find that ResNet enables(More)