@InProceedings{XialeiLiu2018,
author="Xialei Liu
and Joost Van de Weijer
and Andrew Bagdanov",
title="Leveraging Unlabeled Data for Crowd Counting by Learning to Rank",
booktitle="31st IEEE Conference on Computer Vision and Pattern Recognition",
year="2018",
pages="7661--7669",
optkeywords="Task analysis",
optkeywords="Training",
optkeywords="Computer vision",
optkeywords="Visualization",
optkeywords="Estimation",
optkeywords="Head",
optkeywords="Context modeling",
abstract="We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking ofcropped images , we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same number or fewer persons than the super-image. This allows us to address the problem of limited size of existingdatasets for crowd counting. We collect two crowd scene datasets from Google using keyword searches and queryby-example image retrieval, respectively. We demonstrate how to efficiently learn from these unlabeled datasets by incorporating learning-to-rank in a multi-task network which simultaneously ranks images and estimates crowd density maps. Experiments on two of the most challenging crowd counting datasets show that our approach obtains state-ofthe-art results.",
optnote="LAMP; 600.109; 600.106; 600.120",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3159), last updated on Tue, 08 Feb 2022 14:03:24 +0100",
doi="10.1109/CVPR.2018.00799",
file=":http://refbase.cvc.uab.es/files/LWB2018.pdf:PDF"
}