activity
20222025
most citedSpatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration

4 citations · 12 across the 17 of their papers we have counts for

collaborators

17 papers

cs.CV2025

DualX-VSR: Dual Axial SpatialTemporal Transformer for Real-World Video Super-Resolution without Motion Compensation

Shuo Cao, Yihao Liu, Xiaohui Li +3

Transformer-based models like ViViT and TimeSformer have advanced video understanding by effectively modeling spatiotemporal dependencies. Recent video generation models, such as S…

cs.CV2025

Pretraining Deformable Image Registration Networks with Random Images

Junyu Chen, Shuwen Wei, Yihao Liu +2

Recent advances in deep learning-based medical image registration have shown that training deep neural networks~(DNNs) does not necessarily require medical images. Previous work sh…

eess.IV2025

Correlation Ratio for Unsupervised Learning of Multi-modal Deformable Registration

Xiaojian Chen, Yihao Liu, Shuwen Wei +3

In recent years, unsupervised learning for deformable image registration has been a major research focus. This approach involves training a registration network using pairs of movi…

cs.CL2025

TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models

Deyin Yi, Yihao Liu, Lang Cao +4

Tabular data analysis is crucial in many scenarios, yet efficiently identifying the most relevant data analysis queries and results for a new table remains a significant challenge.…

cs.LG20241 cited

WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning

Xiangyu Zhao, Zhiwang Zhou, Wenlong Zhang +9

The Earth's weather system encompasses intricate weather data modalities and diverse weather understanding tasks, which hold significant value to human life. Existing data-driven m…

cs.CV2024

Learning A Low-Level Vision Generalist via Visual Task Prompt

Xiangyu Chen, Yihao Liu, Yuandong Pu +4

Building a unified model for general low-level vision tasks holds significant research and practical value. Current methods encounter several critical issues. Multi-task restoratio…