42 citations · 67 across the 5 of their papers we have counts for
5 papers
What's Wrong with the Bottom-up Methods in Arbitrary-shape Scene Text Detection
Chengpei Xu, Wenjing Jia, Tingcheng Cui +3
The latest trend in the bottom-up perspective for arbitrary-shape scene text detection is to reason the links between text segments using Graph Convolutional Network (GCN). Notwith…
SFSegNet: Parse Freehand Sketches using Deep Fully Convolutional Networks
Junkun Jiang, Ruomei Wang, Shujin Lin +1
Parsing sketches via semantic segmentation is attractive but challenging, because (i) free-hand drawings are abstract with large variances in depicting objects due to different dra…
Multi-column Point-CNN for Sketch Segmentation
Fei Wang, Shujin Lin, Hanhui Li +4
Traditional sketch segmentation methods mainly rely on handcrafted features and complicate models, and their performance is far from satisfactory due to the abstract representation…
Structured Inhomogeneous Density Map Learning for Crowd Counting
Hanhui Li, Xiangjian He, Hefeng Wu +4
In this paper, we aim at tackling the problem of crowd counting in extremely high-density scenes, which contain hundreds, or even thousands of people. We begin by a comprehensive a…
Learning Deep Similarity Models with Focus Ranking for Fabric Image Retrieval
Daiguo Deng, Ruomei Wang, Hefeng Wu +3
Fabric image retrieval is beneficial to many applications including clothing searching, online shopping and cloth modeling. Learning pairwise image similarity is of great importanc…