19 citations · 54 across the 13 of their papers we have counts for
13 papers
Learning from Multi-Perception Features for Real-Word Image Super-resolution
Axi Niu, Kang Zhang, Trung X. Pham +4
Currently, there are two popular approaches for addressing real-world image super-resolution problems: degradation-estimation-based and blind-based methods. However, degradation-es…
GRAN: Ghost Residual Attention Network for Single Image Super Resolution
Axi Niu, Pei Wang, Yu Zhu +3
Recently, many works have designed wider and deeper networks to achieve higher image super-resolution performance. Despite their outstanding performance, they still suffer from hig…
Take a Prior from Other Tasks for Severe Blur Removal
Pei Wang, Danna Xue, Yu Zhu +4
Recovering clear structures from severely blurry inputs is a challenging problem due to the large movements between the camera and the scene. Although some works apply segmentation…
SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision
Danna Xue, Fei Yang, Pei Wang +4
Accurate semantic segmentation models typically require significant computational resources, inhibiting their use in practical applications. Recent works rely on well-crafted light…
Learning Distinctive Margin toward Active Domain Adaptation
Ming Xie, Yuxi Li, Yabiao Wang +6
Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under unsupervised or few-shot semi-supervised settings, recently the solution of active learning…
Omni-DETR: Omni-Supervised Object Detection with Transformers
Pei Wang, Zhaowei Cai, Hao Yang +4
We consider the problem of omni-supervised object detection, which can use unlabeled, fully labeled and weakly labeled annotations, such as image tags, counts, points, etc., for ob…