35 citations · 81 across the 7 of their papers we have counts for
13 papers
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution
Xin Li, Xin Jin, Tao Yu +4
Traditional single image super-resolution (SISR) methods that focus on solving single and uniform degradation (i.e., bicubic down-sampling), typically suffer from poor performance…
FAN: Frequency Aggregation Network for Real Image Super-resolution
Yingxue Pang, Xin Li, Xin Jin +4
Single image super-resolution (SISR) aims to recover the high-resolution (HR) image from its low-resolution (LR) input image. With the development of deep learning, SISR has achiev…
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…
Global Distance-distributions Separation for Unsupervised Person Re-identification
Xin Jin, Cuiling Lan, Wenjun Zeng +1
Supervised person re-identification (ReID) often has poor scalability and usability in real-world deployments due to domain gaps and the lack of annotations for the target domain d…
Feature Alignment and Restoration for Domain Generalization and Adaptation
Xin Jin, Cuiling Lan, Wenjun Zeng +1
For domain generalization (DG) and unsupervised domain adaptation (UDA), cross domain feature alignment has been widely explored to pull the feature distributions of different doma…
Style Normalization and Restitution for Generalizable Person Re-identification
Xin Jin, Cuiling Lan, Wenjun Zeng +2
Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps. The key to solving this problem lies in…