85 citations · 190 across the 4 of their papers we have counts for
6 papers
Large-Field Contextual Feature Learning for Glass Detection
Haiyang Mei, Xin Yang, Letian Yu +3
Glass is very common in our daily life. Existing computer vision systems neglect it and thus may have severe consequences, e.g., a robot may crash into a glass wall. However, sensi…
Progressive Glass Segmentation
Letian Yu, Haiyang Mei, Wen Dong +4
Glass is very common in the real world. Influenced by the uncertainty about the glass region and the varying complex scenes behind the glass, the existence of glass poses severe ch…
A Two-Stage Attentive Network for Single Image Super-Resolution
Jiqing Zhang, Chengjiang Long, Yuxin Wang +4
Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and contribute remarkable progress. However, most of the exist…
Camouflaged Object Segmentation with Distraction Mining
Haiyang Mei, Ge-Peng Ji, Ziqi Wei +3
Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings, which has a wide range of valuable applications. The key cha…
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors
Xin Yang, Haiyang Mei, Jiqing Zhang +4
Recently, single-image super-resolution has made great progress owing to the development of deep convolutional neural networks (CNNs). The vast majority of CNN-based models use a p…
Where Is My Mirror?
Xin Yang, Haiyang Mei, Ke Xu +3
Mirrors are everywhere in our daily lives. Existing computer vision systems do not consider mirrors, and hence may get confused by the reflected content inside a mirror, resulting…