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20192026
most citedDelving Globally into Texture and Structure for Image Inpainting

26 citations · 70 across the 18 of their papers we have counts for

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Showing 2019Show all

8 papers · 1 filter

cs.LG20191 cited

Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space

Jiahuan Ren, Zhao Zhang, Sheng Li +4

In this paper, we investigate the robust dictionary learning (DL) to discover the hybrid salient low-rank and sparse representation in a factorized compressed space. A Joint Robust…

cs.CV2019

Compressed DenseNet for Lightweight Character Recognition

Zhao Zhang, Zemin Tang, Yang Wang +3

Convolutional Recurrent Neural Network (CRNN) is a popular network for recognizing texts in images. Advances like the variant of CRNN, such as Dense Convolutional Network with Conn…

cs.CV2019

DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking

Yanyan Wei, Zhao Zhang, Yang Wang +4

Single image deraining (SID) is an important and challenging topic in emerging vision applications, and most of emerged deraining methods are supervised relying on the ground truth…

cs.CV20191 cited

Diversifying Inference Path Selection: Moving-Mobile-Network for Landmark Recognition

Biao Qian, Yang Wang, Zhao Zhang +3

Deep convolutional neural networks have largely benefited computer vision tasks. However, the high computational complexity limits their real-world applications. To this end, many…

cs.LG2019

Kernelized Multiview Subspace Analysis by Self-weighted Learning

Huibing Wang, Yang Wang, Zhao Zhang +4

With the popularity of multimedia technology, information is always represented or transmitted from multiple views. Most of the existing algorithms are graph-based ones to learn th…

cs.CV2019

Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery

Zhao Zhang, Lei Wang, Sheng Li +4

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover th…