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Understanding the impact of mistakes on background regions in crowd
  counting

Understanding the impact of mistakes on background regions in crowd counting

IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2020
30 March 2020
Davide Modolo
Bing Shuai
Rahul Rama Varior
Joseph Tighe
ArXiv (abs)PDFHTML

Papers citing "Understanding the impact of mistakes on background regions in crowd counting"

6 / 6 papers shown
Regressor-Segmenter Mutual Prompt Learning for Crowd Counting
Regressor-Segmenter Mutual Prompt Learning for Crowd CountingComputer Vision and Pattern Recognition (CVPR), 2023
Mingyue Guo
Li Yuan
Zhaoyi Yan
Binghui Chen
Yaowei Wang
QiXiang Ye
435
24
0
04 Dec 2023
Training-free Object Counting with Prompts
Training-free Object Counting with PromptsIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2023
Zenglin Shi
Ying Sun
Mengmi Zhang
VLM
425
43
0
30 Jun 2023
Focus for Free in Density-Based Counting
Focus for Free in Density-Based CountingInternational Journal of Computer Vision (IJCV), 2023
Zenglin Shi
Pascal Mettes
Cees G. M. Snoek
3DPC
261
19
0
08 Jun 2023
HDNet: A Hierarchically Decoupled Network for Crowd Counting
HDNet: A Hierarchically Decoupled Network for Crowd CountingIEEE International Conference on Multimedia and Expo (ICME), 2022
Chenliang Gu
Changan Wang
Bin-Bin Gao
Jun Liu
Tianliang Zhang
122
1
0
12 Dec 2022
Counting with Adaptive Auxiliary Learning
Counting with Adaptive Auxiliary Learning
Y. Meng
J. Bridge
Meng Wei
Yitian Zhao
Yihong Qiao
Xiaoyun Yang
Xiaowei Huang
Yalin Zheng
277
5
0
08 Mar 2022
A Survey on Deep Learning-based Single Image Crowd Counting: Network
  Design, Loss Function and Supervisory Signal
A Survey on Deep Learning-based Single Image Crowd Counting: Network Design, Loss Function and Supervisory SignalNeurocomputing (Neurocomputing), 2020
Haoyue Bai
Jiageng Mao
Shueng-Han Gary Chan
437
29
0
31 Dec 2020
1
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