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The mainstream crowd counting methods usually utilize the convolution neural network (CNN) to regress a density map, requiring point-level annotations.
Privacy preserving crowd monitoring: Counting people without people models or tracking
Antoni B Chan, Zhang-Sheng John Liang, and Nuno Vasconcelos · 2008
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Multi-source multi-scale counting in extremely dense crowd images
Haroon Idrees, Imran Saleemi, Cody Seibert, and Mubarak Shah · 2013
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Leveraging gps-less sensing scheduling for green mobile crowd sensing
Xiang Sheng, Jian Tang, Xuejie Xiao, and Guoliang Xue · 2014
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Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm
Bin Guo, Zhu Wang, Zhiwen Yu, Yu Wang, Neil Y Yen, Runhe Huang, and Xingshe Zhou · 2015
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Adam: A method for stochastic optimization 3rd international conference on learning representations
DP Kingma and JL Ba · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Cross-scene crowd counting via deep convolutional neural networks
Cong Zhang, Hongsheng Li, Xiaogang Wang, and Xiaokang Yang · 2015
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Bridging nonlinearities and stochastic regularizers with gaussian error linear units
Dan Hendrycks and Kevin Gimpel · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
Earlier work this paper cites.
Gaussian process density counting from weak supervision
Matthias von Borstel, Melih Kandemir, Philip Schmidt, Madhavi K Rao, Kumar Rajamani, and Fred A Hamprecht · 2016
Earlier work this paper cites.
Single-image crowd counting via multi-column convolutional neural network
Yingying Zhang, Desen Zhou, Siqin Chen, Shenghua Gao, and Yi Ma · 2016
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Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting
Vishwanath A Sindagi and Vishal M Patel · 2017
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Generating high-quality crowd density maps using contextual pyramid cnns
Vishwanath A Sindagi and Vishal M Patel · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Fcn-rlstm: Deep spatio-temporal neural networks for vehicle counting in city cameras
Shanghang Zhang, Guanhang Wu, Joao P Costeira, and José MF Moura · 2017
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Scale aggregation network for accurate and efficient crowd counting
Xinkun Cao, Zhipeng Wang, Yanyun Zhao, and Fei Su · 2018
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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
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CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes
Yuhong Li, Xiaofan Zhang, and Deming Chen · 2018
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Decidenet: counting varying density crowds through attention guided detection and density estimation
Jiang Liu, Chenqiang Gao, Deyu Meng, and Alexander G Hauptmann · 2018
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Iterative crowd counting
Viresh Ranjan, Hieu Le, and Minh Hoai · 2018
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Crowd counting with deep negative correlation learning
Zenglin Shi, Le Zhang, Yun Liu, Xiaofeng Cao, Yangdong Ye, Ming-Ming Cheng, and Guoyan Zheng · 2018
Earlier work this paper cites.
Cˆ 3 framework: An open-source pytorch code for crowd counting
Junyu Gao, Wei Lin, Bin Zhao, Dong Wang, Chenyu Gao, and Jun Wen · 2019
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Pcc net: Perspective crowd counting via spatial convolutional network
Junyu Gao, Qi Wang, and Xuelong Li · 2019
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Crowd counting and density estimation by trellis encoder-decoder networks
Xiaolong Jiang, Zehao Xiao, Baochang Zhang, Xiantong Zhen, Xianbin Cao, David Doermann, and Ling Shao · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 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.
Adcrowdnet: An attention-injective deformable convolutional network for crowd understanding
Ning Liu, Yongchao Long, Changqing Zou, Qun Niu, Li Pan, and Hefeng Wu · 2019
Cited alongside, same era.
Context-aware crowd counting
Weizhe Liu, Mathieu Salzmann, and Pascal Fua · 2019
Cited alongside, same era.
Exploiting unlabeled data in cnns by self-supervised learning to rank
Locate, size and count: Accurately resolving people in dense crowds via detection
Deepak Babu Sam, Skand Vishwanath Peri, Mukuntha Narayanan Sundararaman, Amogh Kamath, and Venkatesh Babu Radhakrishnan · 2020
Later among the works it cites.
Jhu-crowd++: Large-scale crowd counting dataset and a benchmark method
Vishwanath Sindagi, Rajeev Yasarla, and Vishal MM Patel · 2020
Later among the works it cites.
Modeling noisy annotations for crowd counting
Jia Wan and Antoni Chan · 2020
Later among the works it cites.
Kernel-based density map generation for dense object counting
Jia Wan, Qingzhong Wang, and Antoni B Chan · 2020
Later among the works it cites.
Distribution matching for crowd counting
Boyu Wang, Huidong Liu, Dimitris Samaras, and Minh Hoai · 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
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Xialei Liu, Joost Van De Weijer, and Andrew D Bagdanov · 2019
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Cited alongside, same era.
Point in, box out: Beyond counting persons in crowds
Yuting Liu, Miaojing Shi, Qijun Zhao, and Xiaofang Wang · 2019
Cited alongside, same era.
Bayesian loss for crowd count estimation with point supervision
Zhiheng Ma, Xing Wei, Xiaopeng Hong, and Yihong Gong · 2019
Cited alongside, same era.
Revisiting perspective information for efficient crowd counting
Miaojing Shi, Zhaohui Yang, Chao Xu, and Qijun Chen · 2019
Cited alongside, same era.
Counting with focus for free
Zenglin Shi, Pascal Mettes, and Cees GM Snoek · 2019
Cited alongside, same era.
Multi-level bottom-top and top-bottom feature fusion for crowd counting
Vishwanath A Sindagi and Vishal M Patel · 2019
Cited alongside, same era.
Later among the works it cites.
Reverse perspective network for perspective-aware object counting
Yifan Yang, Guorong Li, Zhe Wu, Li Su, Qingming Huang, and Nicu Sebe · 2020
Later among the works it cites.
Weakly-supervised crowd counting learns from sorting rather than locations
Yifan Yang, Guorong Li, Zhe Wu, Li Su, Qingming Huang, and Nicu Sebe · 2020
Later among the works it cites.
Deformable detr: Deformable transformers for end-to-end object detection
Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai · 2020
Later among the works it cites.
Localization in the crowd with topological constraints
Shahira Abousamra, Minh Hoai, Dimitris Samaras, and Chao Chen · 2021
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Pre-trained image processing transformer
Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao · 2021
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Cell localization and counting using direction field map
Yajie Chen, Dingkang Liang, Xiang Bai, Yongchao Xu, and Xin Yang · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexander Kolesnikov, Alexey Dosovitskiy, Dirk Weissenborn, Georg Heigold, Jakob Uszkoreit, Lucas Beyer, Matthias Minderer, Mostafa Dehghani, Neil Houlsby, Sylvain Gelly, et al · 2021
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Towards using count-level weak supervision for crowd counting
Yinjie Lei, Yan Liu, Pingping Zhang, and Lingqiao Liu · 2021
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Focal inverse distance transform maps for crowd localization and counting in dense crowd
Dingkang Liang, Wei Xu, Yingying Zhu, and Yu Zhou · 2021
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Direct measure matching for crowd counting
Hui Lin, Xiaopeng Hong, Zhiheng Ma, Xing Wei, Yunfeng Qiu, Yaowei Wang, and Yihong Gong · 2021
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Visdrone-cc2021: The vision meets drone crowd counting challenge results
Zhihao Liu, Zhijian He, Lujia Wang, Wenguan Wang, Yixuan Yuan, Dingwen Zhang, Jinglin Zhang, Pengfei Zhu, Luc Van Gool, Junwei Han, et al · 2021
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Learning to count via unbalanced optimal transport
Zhiheng Ma, Xing Wei, Xiaopeng Hong, Hui Lin, Yunfeng Qiu, and Yihong Gong · 2021
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Transformer meets tracker: Exploiting temporal context for robust visual tracking
Ning Wang, Wengang Zhou, Jie Wang, and Houqiang Li · 2021
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Dilated-scale-aware category-attention convnet for multi-class object counting
Wei Xu, Dingkang Liang, Yixiao Zheng, Jiahao Xie, and Zhanyu Ma · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al · 2021
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Autoscale: Learning to scale for crowd counting
Chenfeng Xu, Dingkang Liang, Yongchao Xu, Song Bai, Wei Zhan, Xiang Bai, and Masayoshi Tomizuka · 2022
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