521 citations · 659 across the 11 of their papers we have counts for
11 papers
Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens
Yuxiao Chen, Jianbo Yuan, Yu Tian +5
Contrastive learning-based vision-language pre-training approaches, such as CLIP, have demonstrated great success in many vision-language tasks. These methods achieve cross-modal a…
HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention
Shijie Geng, Jianbo Yuan, Yu Tian +2
The success of large-scale contrastive vision-language pretraining (CLIP) has benefited both visual recognition and multimodal content understanding. The concise design brings CLIP…
Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning
Yuxiao Chen, Long Zhao, Jianbo Yuan +5
Despite the success of fully-supervised human skeleton sequence modeling, utilizing self-supervised pre-training for skeleton sequence representation learning has been an active fi…
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…
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…
Dual Projection Generative Adversarial Networks for Conditional Image Generation
Ligong Han, Martin Renqiang Min, Anastasis Stathopoulos +4
Conditional Generative Adversarial Networks (cGANs) extend the standard unconditional GAN framework to learning joint data-label distributions from samples, and have been establish…
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…
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…
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…
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…
CR-GAN: Learning Complete Representations for Multi-view Generation
Yu Tian, Xi Peng, Long Zhao +2
Generating multi-view images from a single-view input is an essential yet challenging problem. It has broad applications in vision, graphics, and robotics. Our study indicates that…