285 citations · 1.2k across the 56 of their papers we have counts for
12 papers · 1 filter
Learning Regional Attraction for Line Segment Detection
Nan Xue, Song Bai, Fu-Dong Wang +4
This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map…
AutoScale: Learning to Scale for Crowd Counting and Localization
Chenfeng Xu, Dingkang Liang, Yongchao Xu +4
Recent works on crowd counting mainly leverage CNNs to count by regressing density maps, and have achieved great progress. In the density map, each person is represented by a Gauss…
Anchor Diffusion for Unsupervised Video Object Segmentation
Zhao Yang, Qiang Wang, Luca Bertinetto +3
Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow. Despite their complexity, these kinds of approaches te…
Asymmetric Non-local Neural Networks for Semantic Segmentation
Zhen Zhu, Mengde Xu, Song Bai +2
The non-local module works as a particularly useful technique for semantic segmentation while criticized for its prohibitive computation and GPU memory occupation. In this paper, w…
View N-gram Network for 3D Object Retrieval
Xinwei He, Tengteng Huang, Song Bai +1
How to aggregate multi-view representations of a 3D object into an informative and discriminative one remains a key challenge for multi-view 3D object retrieval. Existing methods e…
Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting
Chenfeng Xu, Kai Qiu, Jianlong Fu +3
Dense crowd counting aims to predict thousands of human instances from an image, by calculating integrals of a density map over image pixels. Existing approaches mainly suffer from…