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Yanwen Guo
Researcher at Nanjing University
Publications - 132
Citations - 3018
Yanwen Guo is an academic researcher from Nanjing University. The author has contributed to research in topics: Computer science & Image segmentation. The author has an hindex of 18, co-authored 113 publications receiving 1992 citations. Previous affiliations of Yanwen Guo include Nanjing Tech University & Dalian Naval Academy.
Papers
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Proceedings ArticleDOI
Multi-Label Image Recognition With Graph Convolutional Networks
TL;DR: This work proposes a multi-label classification model based on Graph Convolutional Network (GCN), and proposes a novel re-weighted scheme to create an effective label correlation matrix to guide information propagation among the nodes in GCN.
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Multi-Label Image Recognition with Graph Convolutional Networks
TL;DR: Zhang et al. as mentioned in this paper proposed a multi-label classification model based on Graph Convolutional Network (GCN), where each node (label) is represented by word embeddings of a label, and GCN is learned to map this label graph into a set of inter-dependent object classifiers.
Proceedings ArticleDOI
Fast Non-Local Algorithm for Image Denoising
TL;DR: This paper introduces an approximate measure about the similarity of neighborhood windows, then uses an efficient summed square image (SSI) scheme and fast Fourier transform (FFT) to accelerate the calculation of this measure.
Journal ArticleDOI
Image Retargeting Using Mesh Parametrization
TL;DR: This paper associates image saliency into the image mesh and regard image structure as constraints for mesh parametrization to emphasize salient objects and minimize visual distortion in image retargeting.
Journal ArticleDOI
Denoising of Hyperspectral Images Using Nonconvex Low Rank Matrix Approximation
TL;DR: This paper reformulates the approximation problem using nonconvex regularizer instead of the traditional nuclear norm, resulting in a tighter approximation of the original sparsity-regularised rank function and develops an iterative algorithm based on the augmented Lagrangian multipliers method that can preserve large-scale image structures and small-scale details well.