121 citations · 237 across the 7 of their papers we have counts for
10 papers
Enhancing and Dissecting Crowd Counting By Synthetic Data
Yi Hou, Chengyang Li, Yuheng Lu +4
In this article, we propose a simulated crowd counting dataset CrowdX, which has a large scale, accurate labeling, parameterized realization, and high fidelity. The experimental re…
BBA-net: A bi-branch attention network for crowd counting
Yi Hou, Chengyang Li, Fan Yang +5
In the field of crowd counting, the current mainstream CNN-based regression methods simply extract the density information of pedestrians without finding the position of each perso…
Correlating Edge, Pose with Parsing
Ziwei Zhang, Chi Su, Liang Zheng +1
According to existing studies, human body edge and pose are two beneficial factors to human parsing. The effectiveness of each of the high-level features (edge and pose) is confirm…
FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
Xu Qin, Zhilin Wang, Yuanchao Bai +2
In this paper, we propose an end-to-end feature fusion at-tention network (FFA-Net) to directly restore the haze-free image. The FFA-Net architecture consists of three key componen…
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior
Yuanchao Bai, Huizhu Jia, Ming Jiang +3
Blind image deblurring is a challenging problem in computer vision, which aims to restore both the blur kernel and the latent sharp image from only a blurry observation. Inspired b…
Attention Driven Person Re-identification
Fan Yang, Ke Yan, Shijian Lu +3
Person re-identification (ReID) is a challenging task due to arbitrary human pose variations, background clutters, etc. It has been studied extensively in recent years, but the mul…