Discovering regression-detection bi-knowledge transfer for unsupervised cross-domain crowd counting-Reference-Cited by-同舟云学术

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Discovering regression-detection bi-knowledge transfer for unsupervised cross-domain crowd counting

Published:2022-07 Issue: Volume:494 Page:418-431
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Liu Yuting,Wang Zheng,Shi Miaojing,Satoh Shin’ichi,Zhao Qijun,Yang Hongyu

Funder

Japan Society for the Promotion of Science

National Natural Science Foundation of China

Japan Science and Technology Agency

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference54 articles.

1. H. Fu, H. Ma, H. Xiao, Crowd counting via head detection and motion flow estimation, in: Proceedings of the ACM International Conference on Multimedia (ACM MM), 2014, pp. 877–880.

2. L. Boominathan, S.S. Kruthiventi, R.V. Babu, Crowdnet: A deep convolutional network for dense crowd counting, in: Proceedings of the ACM International Conference on Multimedia (ACM MM), 2016, pp. 640–644.

3. X. Tan, C. Tao, T. Ren, J. Tang, G. Wu, Crowd counting via multi-layer regression, in: Proceedings of the ACM International Conference on Multimedia (ACM MM), 2019, pp. 1907–1915.

4. D. Guo, K. Li, Z. Zha, M. Wang, Dadnet: Dilated-attention-deformable convnet for crowd counting, in: Proceedings of the ACM International Conference on Multimedia (ACM MM), 2019, pp. 1823–1832.

5. Y. Liu, M. Shi, Q. Zhao, X. Wang, Point in, box out: Beyond counting persons in crowds, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 6469–6478.

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