8 citations · 18 across the 4 of their papers we have counts for
6 papers
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…
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…
Local Patch AutoAugment with Multi-Agent Collaboration
Shiqi Lin, Tao Yu, Ruoyu Feng +3
Data augmentation (DA) plays a critical role in improving the generalization of deep learning models. Recent works on automatically searching for DA policies from data have achieve…
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…
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…
Progressive Image Inpainting with Full-Resolution Residual Network
Zongyu Guo, Zhibo Chen, Tao Yu +2
Recently, learning-based algorithms for image inpainting achieve remarkable progress dealing with squared or irregular holes. However, they fail to generate plausible textures insi…