116 citations · 336 across the 7 of their papers we have counts for
7 papers
Hyperspectral Target Detection Based on Low-Rank Background Subspace Learning and Graph Laplacian Regularization
Dunbin Shen, Xiaorui Ma, Wenfeng Kong +2
Hyperspectral target detection is good at finding dim and small objects based on spectral characteristics. However, existing representation-based methods are hindered by the proble…
Deep Multiphase Level Set for Scene Parsing
Pingping Zhang, Wei Liu, Yinjie Lei +2
Recently, Fully Convolutional Network (FCN) seems to be the go-to architecture for image segmentation, including semantic scene parsing. However, it is difficult for a generic FCN…
Video Person Re-identification by Temporal Residual Learning
Ju Dai, Pingping Zhang, Huchuan Lu +1
In this paper, we propose a novel feature learning framework for video person re-identification (re-ID). The proposed framework largely aims to exploit the adequate temporal inform…
Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps
Pingping Zhang, Wei Liu, Dong Wang +4
In this paper, we propose a novel effective non-rigid object tracking framework based on the spatial-temporal consistent saliency detection. In contrast to most existing trackers t…
Learning Uncertain Convolutional Features for Accurate Saliency Detection
Pingping Zhang, Dong Wang, Huchuan Lu +2
Deep convolutional neural networks (CNNs) have delivered superior performance in many computer vision tasks. In this paper, we propose a novel deep fully convolutional network mode…
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
Pingping Zhang, Dong Wang, Huchuan Lu +2
Fully convolutional neural networks (FCNs) have shown outstanding performance in many dense labeling problems. One key pillar of these successes is mining relevant information from…
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
Yifan Wang, Lijun Wang, Hongyu Wang +1
One impressive advantage of convolutional neural networks (CNNs) is their ability to automatically learn feature representation from raw pixels, eliminating the need for hand-desig…