76 citations · 188 across the 14 of their papers we have counts for
6 papers · 1 filter
Incremental Embedding Learning via Zero-Shot Translation
Kun Wei, Cheng Deng, Xu Yang +1
Modern deep learning methods have achieved great success in machine learning and computer vision fields by learning a set of pre-defined datasets. Howerver, these methods perform u…
Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation
Chenxin Xu, Siheng Chen, Maosen Li +1
We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learnin…
Sampling and Recovery of Graph Signals based on Graph Neural Networks
Siheng Chen, Maosen Li, Ya Zhang
We propose interpretable graph neural networks for sampling and recovery of graph signals, respectively. To take informative measurements, we propose a new graph neural sampling mo…
Graph Cross Networks with Vertex Infomax Pooling
Maosen Li, Siheng Chen, Ya Zhang +1
We propose a novel graph cross network (GXN) to achieve comprehensive feature learning from multiple scales of a graph. Based on trainable hierarchical representations of a graph,…
Decoupled Variational Embedding for Signed Directed Networks
Xu Chen, Jiangchao Yao, Maosen Li +2
Node representation learning for signed directed networks has received considerable attention in many real-world applications such as link sign prediction, node classification and…
Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction
Maosen Li, Siheng Chen, Yangheng Zhao +3
We propose novel dynamic multiscale graph neural networks (DMGNN) to predict 3D skeleton-based human motions. The core idea of DMGNN is to use a multiscale graph to comprehensively…