76 citations · 188 across the 14 of their papers we have counts for
14 papers
Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction
Maosen Li, Siheng Chen, Zijing Zhang +3
Graph convolutional network based methods that model the body-joints' relations, have recently shown great promise in 3D skeleton-based human motion prediction. However, these meth…
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning
Chenxin Xu, Maosen Li, Zhenyang Ni +2
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works only con…
Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction
Maosen Li, Siheng Chen, Yangheng Zhao +3
We propose a multiscale spatio-temporal graph neural network (MST-GNN) to predict the future 3D skeleton-based human poses in an action-category-agnostic manner. The core of MST-GN…
Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network
Maosen Li, Siheng Chen, Yanning Shen +3
This paper considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system. We propose a novel collaborative predictio…
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…
Attribute Restoration Framework for Anomaly Detection
Chaoqin Huang, Fei Ye, Jinkun Cao +3
With the recent advances in deep neural networks, anomaly detection in multimedia has received much attention in the computer vision community. While reconstruction-based methods h…
Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction
Maosen Li, Siheng Chen, Xu Chen +3
3D skeleton-based action recognition and motion prediction are two essential problems of human activity understanding. In many previous works: 1) they studied two tasks separately,…
Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition
Maosen Li, Siheng Chen, Xu Chen +3
Action recognition with skeleton data has recently attracted much attention in computer vision. Previous studies are mostly based on fixed skeleton graphs, only capturing local phy…
SNR-based adaptive acquisition method for fast Fourier ptychographic microscopy
An Pan, Yan Zhang, Maosen Li +4
Fourier ptychographic microscopy (FPM) is a computational imaging technique with both high resolution and large field-of-view. However, the effective numerical aperture (NA) achiev…