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In this paper, we propose a simple yet effective crowd counting and localization network named SCALNet.
“Very deep convolutional networks for large-scale image recognition,”
Karen Simonyan and Andrew Zisserman, · 2014
Earlier work this paper cites.
“Single-image crowd counting via multi-column convolutional neural network,”
Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, · 2016
Earlier work this paper cites.
“Faster r-cnn: Towards real-time object detection with region proposal networks,”
S. Ren, K. He, R. Girshick, and J. Sun, · 2017
Earlier work this paper cites.
“Finding tiny faces,”
Peiyun Hu and Deva Ramanan, · 2017
Earlier work this paper cites.
“Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes,”
Yuhong Li, Xiaofan Zhang, and Deming Chen, · 2018
Earlier work this paper cites.
“Scale aggregation network for accurate and efficient crowd counting,”
Xinkun Cao, Zhipeng Wang, Yanyun Zhao, and Fei Su, · 2018
Earlier work this paper cites.
“Where are the blobs: Counting by localization with point supervision,”
Issam H Laradji, Negar Rostamzadeh, Pedro O Pinheiro, David Vazquez, and Mark Schmidt, · 2018
Earlier work this paper cites.
“Composition loss for counting, density map estimation and localization in dense crowds,”
Haroon Idrees, Muhmmad Tayyab, Kishan Athrey, Dong Zhang, Somaya Al-Maadeed, Nasir Rajpoot, and Mubarak Shah, · 2018
Earlier work this paper cites.
“Deep layer aggregation,”
F. Yu, D. Wang, E. Shelhamer, and T. Darrell, · 2018
Earlier work this paper cites.
“Recurrent attentive zooming for joint crowd counting and precise localization,”
C. Liu, X. Weng, and Y. Mu, · 2019
Cited alongside, same era.
“Crowd counting with deep structured scale integration network,”
Lingbo Liu, Zhilin Qiu, Guanbin Li, Shufan Liu, Wanli Ouyang, and Liang Lin, · 2019
Cited alongside, same era.
“Object counting in video surveillance using multi-scale density map regression,”
Yi Wang, Junhui Hou, and Lap-Pui Chau, · 2019
Cited alongside, same era.
“Scar: Spatial-/channel-wise attention regression networks for crowd counting,”
Junyu Gao, Qi Wang, and Yuan Yuan, · 2019
Cited alongside, same era.
“Context-aware crowd counting,”
Weizhe Liu, Mathieu Salzmann, and Pascal Fua, · 2019
Cited alongside, same era.
“Bayesian loss for crowd count estimation with point supervision,”
Zhiheng Ma, Xing Wei, Xiaopeng Hong, and Yihong Gong, · 2019
“Refinenet: Multi-path refinement networks for dense prediction,”
Guosheng Lin, Fayao Liu, Anton Milan, Chunhua Shen, and Ian Reid, · 2019
Later among the works it cites.
Xingyi Zhou, Dequan Wang, and Philipp Krähenbühl, · 2019
Later among the works it cites.
“Cnn-based density estimation and crowd counting: A survey,”
Guangshuai Gao, Junyu Gao, Qingjie Liu, Qi Wang, and Yunhong Wang, · 2020
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“Scale-aware rolling fusion network for crowd counting,”
Ying Chen, Chengying Gao, Zhuo Su, Xiangjian He, and Ning Liu, · 2020
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“Nwpu-crowd: A large-scale benchmark for crowd counting and localization,”
Qi Wang, Junyu Gao, Wei Lin, and Xuelong Li, · 2020
Later among the works it cites.
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Cited alongside, same era.
“Learning from synthetic data for crowd counting in the wild,”
Qi Wang, Junyu Gao, Wei Lin, and Yuan Yuan, · 2019
Cited alongside, same era.
“Point in, box out: Beyond counting persons in crowds,”
Y. Liu, M. Shi, Q. Zhao, and X. Wang, · 2019
Cited alongside, same era.
Junyu Gao, Tao Han, Qi Wang, and Yuan Yuan, · 2019
Cited alongside, same era.
“Going beyond the regression paradigm with accurate dot prediction for dense crowds,”
D. B. Sam, S. Vishwanath Peri, N. S. Mukuntha, and R. Venkatesh Babu, · 2020
Later among the works it cites.
“Locate, size and count: Accurately resolving people in dense crowds via detection,”
Deepak Babu Sam, Skand Vishwanath Peri, Mukuntha Narayanan Sundararaman, Amogh Kamath, and R. Venkatesh Babu, · 2020
Later among the works it cites.
“Focal loss for dense object detection,”
T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, · 2020
Later among the works it cites.
“A self-training approach for point-supervised object detection and counting in crowds,”
Yi Wang, Junhui Hou, Xinyu Hou, and Lap-Pui Chau, · 2021
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