1 code implementation • 13 Oct 2022 • Yanjie Ze, Nicklas Hansen, Yinbo Chen, Mohit Jain, Xiaolong Wang
A prominent approach to visual Reinforcement Learning (RL) is to learn an internal state representation using self-supervised methods, which has the potential benefit of improved sample-efficiency and generalization through additional learning signal and inductive biases.
1 code implementation • 4 Aug 2022 • Yinbo Chen, Xiaolong Wang
Motivated by a generalized formulation of gradient-based meta-learning, we propose a formulation that uses Transformers as hypernetworks for INRs, where it can directly build the whole set of INR weights with Transformers specialized as set-to-set mapping.
1 code implementation • 17 Jun 2022 • Hanzhe Hu, Yinbo Chen, Jiarui Xu, Shubhankar Borse, Hong Cai, Fatih Porikli, Xiaolong Wang
As such, IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions.
1 code implementation • CVPR 2022 • Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang, Humphrey Shi, Xiaolong Wang
The learned implicit neural representation can be decoded to videos of arbitrary spatial resolution and frame rate.
Space-time Video Super-resolution Video Frame Interpolation +1
2 code implementations • CVPR 2021 • Yinbo Chen, Sifei Liu, Xiaolong Wang
How to represent an image?
Ranked #2 on Image Super-Resolution on DIV2K val - 4x upscaling (SSIM metric)
10 code implementations • ICCV 2021 • Yinbo Chen, Zhuang Liu, Huijuan Xu, Trevor Darrell, Xiaolong Wang
The edge between these two lines of works has yet been underexplored, and the effectiveness of meta-learning in few-shot learning remains unclear.
no code implementations • ICLR 2019 • Yuan Li, Xiaodan Liang, Zhiting Hu, Yinbo Chen, Eric P. Xing
Graph neural networks (GNN) have gained increasing research interests as a mean to the challenging goal of robust and universal graph learning.
3 code implementations • CVPR 2019 • Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yu-jia Zhang, Eric P. Xing
Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning.