693 citations · 2.2k across the 82 of their papers we have counts for
16 papers · 1 filter
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…
Spatio-Temporal Graph Scattering Transform
Chao Pan, Siheng Chen, Antonio Ortega
Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenari…
Learning on Attribute-Missing Graphs
Xu Chen, Siheng Chen, Jiangchao Yao +3
Graphs with complete node attributes have been widely explored recently. While in practice, there is a graph where attributes of only partial nodes could be available and those of…
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,…
Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs
Jingchao Su, Xu Chen, Ya Zhang +3
Relational learning aims to make relation inference by exploiting the correlations among different types of entities. Exploring relational learning on multiple bipartite graphs has…
Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph
Chenyang Li, Xu Chen, Ya Zhang +3
Knowledge graphs (KGs) composed of users, objects, and tags are widely used in web applications ranging from E-commerce, social media sites to news portals. This paper concentrates…
Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks
Takuya Fujihashi, Toshiaki Koike-Akino, Siheng Chen +1
In typical point cloud delivery, a sender uses octree-based digital video compression to send three-dimensional (3D) points and color attributes over band-limited links. However, t…
Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising
Siheng Chen, Yonina C. Eldar, Lingxiao Zhao
We propose an interpretable graph neural network framework to denoise single or multiple noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to th…
Control of connectivity and rigidity in prismatic assemblies
Gary P. T. Choi, Siheng Chen, L. Mahadevan
How can we manipulate the topological connectivity of a three-dimensional prismatic assembly to control the number of internal degrees of freedom and the number of connected compon…
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…
MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps
Pengxiang Wu, Siheng Chen, Dimitris Metaxas
The ability to reliably perceive the environmental states, particularly the existence of objects and their motion behavior, is crucial for autonomous driving. In this work, we prop…
Collaborative Motion Prediction via Neural Motion Message Passing
Yue Hu, Siheng Chen, Ya Zhang +1
Motion prediction is essential and challenging for autonomous vehicles and social robots. One challenge of motion prediction is to model the interaction among traffic actors, which…
3D Point Cloud Processing and Learning for Autonomous Driving
Siheng Chen, Baoan Liu, Chen Feng +2
We present a review of 3D point cloud processing and learning for autonomous driving. As one of the most important sensors in autonomous vehicles, light detection and ranging (LiDA…
Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring
Jingxiao Liu, Bingqing Chen, Siheng Chen +3
We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling…
Efficient and Stable Graph Scattering Transforms via Pruning
Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis
Graph convolutional networks (GCNs) have well-documented performance in various graph learning tasks, but their analysis is still at its infancy. Graph scattering transforms (GSTs)…