activity
20182022
most citedLearning Disentangled Feature Representation for Hybrid-distorted Image Restoration

8 citations · 13 across the 6 of their papers we have counts for

collaborators

16 papers

cs.CV20221 cited

Constrained Maximum Cross-Domain Likelihood for Domain Generalization

Jianxin Lin, Yongqiang Tang, Junping Wang +1

As a recent noticeable topic, domain generalization aims to learn a generalizable model on multiple source domains, which is expected to perform well on unseen test domains. Great…

cs.CV2022

Mitigating Both Covariate and Conditional Shift for Domain Generalization

Jianxin Lin, Yongqiang Tang, Junping Wang +1

Domain generalization (DG) aims to learn a model on several source domains, hoping that the model can generalize well to unseen target domains. The distribution shift between domai…

cs.CV2021

Image-to-Image Translation: Methods and Applications

Yingxue Pang, Jianxin Lin, Tao Qin +1

Image-to-image translation (I2I) aims to transfer images from a source domain to a target domain while preserving the content representations. I2I has drawn increasing attention an…

eess.IV2020

LIRA: Lifelong Image Restoration from Unknown Blended Distortions

Jianzhao Liu, Jianxin Lin, Xin Li +3

Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task.…

cs.CV20208 cited

Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration

Xin Li, Xin Jin, Jianxin Lin +5

Hybrid-distorted image restoration (HD-IR) is dedicated to restore real distorted image that is degraded by multiple distortions. Existing HD-IR approaches usually ignore the inher…

cs.CV2020

TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images

Jianxin Lin, Yingxue Pang, Yingce Xia +2

An unsupervised image-to-image translation (UI2I) task deals with learning a mapping between two domains without paired images. While existing UI2I methods usually require numerous…