activity
20182021
most citedRethinking Kernel Methods for Node Representation Learning on Graphs

11 citations · 11 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV2021

AE-StyleGAN: Improved Training of Style-Based Auto-Encoders

Ligong Han, Sri Harsha Musunuri, Martin Renqiang Min +3

StyleGANs have shown impressive results on data generation and manipulation in recent years, thanks to its disentangled style latent space. A lot of efforts have been made in inver…

cs.LG2021

Vision-Aided Beam Tracking: Explore the Proper Use of Camera Images with Deep Learning

Yu Tian, Chenwei Wang

We investigate the problem of wireless beam tracking on mmWave bands with the assistance of camera images. In particular, based on the user's beam indices used and camera images ta…

cs.CV2021

A Good Image Generator Is What You Need for High-Resolution Video Synthesis

Yu Tian, Jian Ren, Menglei Chai +4

Image and video synthesis are closely related areas aiming at generating content from noise. While rapid progress has been demonstrated in improving image-based models to handle la…

cs.LG201911 cited

Rethinking Kernel Methods for Node Representation Learning on Graphs

Yu Tian, Long Zhao, Xi Peng +1

Graph kernels are kernel methods measuring graph similarity and serve as a standard tool for graph classification. However, the use of kernel methods for node classification, which…

cs.CV2019

Semantic Graph Convolutional Networks for 3D Human Pose Regression

Long Zhao, Xi Peng, Yu Tian +2

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convol…

cs.CV2018

Learning to Forecast and Refine Residual Motion for Image-to-Video Generation

Long Zhao, Xi Peng, Yu Tian +2

We consider the problem of image-to-video translation, where an input image is translated into an output video containing motions of a single object. Recent methods for such proble…