34 citations · 111 across the 13 of their papers we have counts for
13 papers
Deep Manifold Learning with Graph Mining
Xuelong Li, Ziheng Jiao, Hongyuan Zhang +1
Admittedly, Graph Convolution Network (GCN) has achieved excellent results on graph datasets such as social networks, citation networks, etc. However, softmax used as the decision…
Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning
Xuelong Li, Hongyuan Zhang, Rui Zhang
The existing matrix completion methods focus on optimizing the relaxation of rank function such as nuclear norm, Schatten-p norm, etc. They usually need many iterations to converge…
AnchorGAE: General Data Clustering via Bipartite Graph Convolution
Hongyuan Zhang, Jiankun Shi, Rui Zhang +1
Since the representative capacity of graph-based clustering methods is usually limited by the graph constructed on the original features, it is attractive to find whether graph neu…
Deep Contrastive Graph Representation via Adaptive Homotopy Learning
Rui Zhang, Chengjun Lu, Ziheng Jiao +1
Homotopy model is an excellent tool exploited by diverse research works in the field of machine learning. However, its flexibility is limited due to lack of adaptiveness, i.e., man…
Shuffle Transformer with Feature Alignment for Video Face Parsing
Rui Zhang, Yang Han, Zilong Huang +4
This is a short technical report introducing the solution of the Team TCParser for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR…
Non-Gradient Manifold Neural Network
Rui Zhang, Ziheng Jiao, Hongyuan Zhang +1
Deep neural network (DNN) generally takes thousands of iterations to optimize via gradient descent and thus has a slow convergence. In addition, softmax, as a decision layer, may i…
WGCN: Graph Convolutional Networks with Weighted Structural Features
Yunxiang Zhao, Jianzhong Qi, Qingwei Liu +1
Graph structural information such as topologies or connectivities provides valuable guidance for graph convolutional networks (GCNs) to learn nodes' representations. Existing GCN m…
LGD-GCN: Local and Global Disentangled Graph Convolutional Networks
Jingwei Guo, Kaizhu Huang, Xinping Yi +1
Disentangled Graph Convolutional Network (DisenGCN) is an encouraging framework to disentangle the latent factors arising in a real-world graph. However, it relies on disentangling…
Enhanced Principal Component Analysis under A Collaborative-Robust Framework
Rui Zhang, Hongyuan Zhang, Xuelong Li
Principal component analysis (PCA) frequently suffers from the disturbance of outliers and thus a spectrum of robust extensions and variations of PCA have been developed. However,…
Unsupervised Graph Embedding via Adaptive Graph Learning
Rui Zhang, Yunxing Zhang, Xuelong Li
Graph autoencoders (GAEs) are powerful tools in representation learning for graph embedding. However, the performance of GAEs is very dependent on the quality of the graph structur…
Adaptive Graph Auto-Encoder for General Data Clustering
Xuelong Li, Hongyuan Zhang, Rui Zhang
Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. H…
Embedding Graph Auto-Encoder for Graph Clustering
Hongyuan Zhang, Rui Zhang, Xuelong Li
Graph clustering, aiming to partition nodes of a graph into various groups via an unsupervised approach, is an attractive topic in recent years. To improve the representative abili…
Exploiting Offset-guided Network for Pose Estimation and Tracking
Rui Zhang, Zheng Zhu, Peng Li +4
Human pose estimation has witnessed a significant advance thanks to the development of deep learning. Recent human pose estimation approaches tend to directly predict the location…