76 citations · 108 across the 10 of their papers we have counts for
13 papers
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…