Label Noise Robust Crowd Counting with Loss Filtering Factor-Reference-Cited by-同舟云学术

Label Noise Robust Crowd Counting with Loss Filtering Factor

Published:2024-03-21 Issue:1 Volume:38 Page:
ISSN:0883-9514
Container-title:Applied Artificial Intelligence
language:en
Short-container-title:Applied Artificial Intelligence

Author:

Xu Zhengmeng¹,Lin Hai²,Chen Yufeng³,Li Yanli⁴

Affiliation:

1. Postdoctoral Research Center, Industrial and Commercial Bank of China, Beijing, China

2. School of Big Data Science, Hebei Finance University, Baoding, China

3. Department of Information Technology, Uppsala University, Uppsala, Sweden

4. School of Electrical and Information Engineering, The University of Sydney, Sydney, Australia

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/08839514.2024.2329859

Reference62 articles.

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3. Cao, X., Z. Wang, Y. Zhao, and F. Su. 2018. “Scale aggregation network for accurate and efficient crowd counting.” In Proceedings of the European conference on computer vision (ECCV), Munich, Germany, 734–50.

4. Chan, A. B., Z.-S. John Liang, and N. Vasconcelos. 2008. “Privacy preserving crowd monitoring: Counting people without people models or tracking.” In 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 1–7.

5. Cheng, Z.-Q., J.-X. Li, Q. Dai, X. Wu, and A. G. Hauptmann. 2019. “Learning spatial awareness to improve crowd counting.” In Proceedings of the IEEE/CVF international conference on computer vision, Seoul, Korea, 6152–61.

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