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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)
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 methods(More)
The `1-minimization (`1-min) used to seek the sparse solution restricts the applicability of compressed sensing. In this study, We use the existing `1-min algorithms as a pre-process step that converts the iterative optimization into linear addition and multiplication operations. This paper then proposes a data separation algorithm with computationally(More)
The applicability and performance of motion detection methods dramatically degrade with the increasing noise. In this paper, we propose a robust dictionary-based background subtraction approach, which formulates background modeling as a linear and sparse combination of atoms in a pre-learned dictionary. Motion detection is then implemented to compare the(More)
The theory of sparse and low-rank representation has worked competitive performance in the field of salient object detection. Generally, the salient object is represented as sparse error while the non-salient region is constrained by the property of low-rank. However, sparsity ignores the global structure which may break up the low-rank property. Besides,(More)
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)