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20162021
most citedHierarchical Representation via Message Propagation for Robust Model Fitting

14 citations · 27 across the 3 of their papers we have counts for

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8 papers · 1 filter

cs.CV202111 cited

Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition

Delian Ruan, Yan Yan, Shenqi Lai +3

In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition. We view the expression information as…

cs.CV202014 cited

Hierarchical Representation via Message Propagation for Robust Model Fitting

Shuyuan Lin, Xing Wang, Guobao Xiao +2

In this paper, we propose a novel hierarchical representation via message propagation (HRMP) method for robust model fitting, which simultaneously takes advantages of both the cons…

cs.CV20192 cited

Hallucinated Adversarial Learning for Robust Visual Tracking

Qiangqiang Wu, Zhihui Chen, Lin Cheng +3

Humans can easily learn new concepts from just a single exemplar, mainly due to their remarkable ability to imagine or hallucinate what the unseen exemplar may look like in differe…

cs.CV2018

DSNet: Deep and Shallow Feature Learning for Efficient Visual Tracking

Qiangqiang Wu, Yan Yan, Yanjie Liang +2

In recent years, Discriminative Correlation Filter (DCF) based tracking methods have achieved great success in visual tracking. However, the multi-resolution convolutional feature…

cs.CV2018

A Fast Face Detection Method via Convolutional Neural Network

Guanjun Guo, Hanzi Wang, Yan Yan +2

Current face or object detection methods via convolutional neural network (such as OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features based on an image pyramid.…

cs.CV2018

A New Target-specific Object Proposal Generation Method for Visual Tracking

Guanjun Guo, Hanzi Wang, Yan Yan +2

Object proposal generation methods have been widely applied to many computer vision tasks. However, existing object proposal generation methods often suffer from the problems of mo…