activity
20182022
most citedFeature Alignment and Restoration for Domain Generalization and Adaptation

35 citations · 188 across the 38 of their papers we have counts for

collaborators

76 papers

cs.CV20222 cited

Semantic-aware Message Broadcasting for Efficient Unsupervised Domain Adaptation

Xin Li, Cuiling Lan, Guoqiang Wei +1

Vision transformer has demonstrated great potential in abundant vision tasks. However, it also inevitably suffers from poor generalization capability when the distribution shift oc…

cs.CV20221 cited

MiNL: Micro-images based Neural Representation for Light Fields

Hanxin Zhu, Henan Wang, Zhibo Chen

Traditional representations for light fields can be separated into two types: explicit representation and implicit representation. Unlike explicit representation that represents li…

cs.CV2022

SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment

Jianzhao Liu, Xin Li, Yanding Peng +2

Image compression has raised widespread interest recently due to its significant importance for multimedia storage and transmission. Meanwhile, a reliable image quality assessment…

eess.IV20211 cited

Task-driven Semantic Coding via Reinforcement Learning

Xin Li, Jun Shi, Zhibo Chen

Task-driven semantic video/image coding has drawn considerable attention with the development of intelligent media applications, such as license plate detection, face detection, an…

cs.CV20217 cited

ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation

Guoqiang Wei, Cuiling Lan, Wenjun Zeng +2

Unsupervised domain adaptive classifcation intends to improve the classifcation performance on unlabeled target domain. To alleviate the adverse effect of domain shift, many approa…

cs.LG20218 cited

PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning

Tao Yu, Cuiling Lan, Wenjun Zeng +3

Learning good feature representations is important for deep reinforcement learning (RL). However, with limited experience, RL often suffers from data inefficiency for training. For…