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20102022
most citedRobust Recovery of Subspace Structures by Low-Rank Representation

3.7k citations · 13.3k across the 162 of their papers we have counts for

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Showing 2016Show all

19 papers · 1 filter

cs.CV2016★ 6 cited

Robust LSTM-Autoencoders for Face De-Occlusion in the Wild

Fang Zhao, Jiashi Feng, Jian Zhao +2

Face recognition techniques have been developed significantly in recent years. However, recognizing faces with partial occlusion is still challenging for existing face recognizers…

cs.CV2016★ 6 cited

Video Scene Parsing with Predictive Feature Learning

Xiaojie Jin, Xin Li, Huaxin Xiao +9

In this work, we address the challenging video scene parsing problem by developing effective representation learning methods given limited parsing annotations. In particular, we co…

cs.CV2016★ 11 cited

Deep Joint Rain Detection and Removal from a Single Image

Wenhan Yang, Robby T. Tan, Jiashi Feng +3

In this paper, we address a rain removal problem from a single image, even in the presence of heavy rain and rain streak accumulation. Our core ideas lie in the new rain image mode…

cs.CV2016★ 43 cited

Visual Processing by a Unified Schatten- Norm and Norm Regularized Principal Component Pursuit

Jing Wang, Meng Wang, Xuegang Hu +1

In this paper, we propose a non-convex formulation to recover the authentic structure from the corrupted real data. Typically, the specific structure is assumed to be low rank, whi…

cs.CV2016★ 6 cited

Multi-stage Object Detection with Group Recursive Learning

Jianan Li, Xiaodan Liang, Jianshu Li +3

Most of existing detection pipelines treat object proposals independently and predict bounding box locations and classification scores over them separately. However, the important…

cs.CV2016★ 13 cited

Multi-Path Feedback Recurrent Neural Network for Scene Parsing

Xiaojie Jin, Yunpeng Chen, Jiashi Feng +2

In this paper, we consider the scene parsing problem and propose a novel Multi-Path Feedback recurrent neural network (MPF-RNN) for parsing scene images. MPF-RNN can enhance the ca…