Web: http://arxiv.org/abs/2205.05844

May 13, 2022, 1:10 a.m. | Shenjian Gong, Shanshan Zhang, Jian Yang, Dengxin Dai, Bernt Schiele

cs.CV updates on arXiv.org arxiv.org

Recently, crowd density estimation has received increasing attention. The
main challenge for this task is to achieve high-quality manual annotations on a
large amount of training data. To avoid reliance on such annotations, previous
works apply unsupervised domain adaptation (UDA) techniques by transferring
knowledge learned from easily accessible synthetic data to real-world datasets.
However, current state-of-the-art methods either rely on external data for
training an auxiliary task or apply an expensive coarse-to-fine estimation. In
this work, we aim to develop …

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