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In recent years, significant progress has been made on the research of crowd counting.
J. Wan, Z. Liu, and A. B. Chan, “A generalized loss function for crowd counting and localization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , June 2021, pp. 1974–1983
1983
Earlier work this paper cites.
S.-F. Lin, J.-Y. Chen, and H.-X. Chao, “Estimation of number of people in crowded scenes using perspective transformation,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans , vol. 31, no. 6, pp. 645–654, 2001
2001
Earlier work this paper cites.
P. Viola, M. J. Jones, and D. Snow, “Detecting pedestrians using patterns of motion and appearance,” International Journal of Computer Vision , vol. 63, no. 2, pp. 153–161, 2005
2005
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B. Leibe, E. Seemann, and B. Schiele, “Pedestrian detection in crowded scenes,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition , vol. 1. IEEE, 2005, pp. 878–885
2005
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B. Wu and R. Nevatia, “Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors,” in Proceedings of the IEEE International Conference on Computer Vision , vol. 1. IEEE, 2005, pp. 90–97
2005
Earlier work this paper cites.
M. Li, Z. Zhang, K. Huang, and T. Tan, “Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection,” in Proceedings of the IEEE International Conference on Pattern Recognition . IEEE, 2008, pp. 1–4
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2009, pp. 248–255
2009
Earlier work this paper cites.
V. Lempitsky and A. Zisserman, “Learning to count objects in images,” Advances in Neural Information Processing Systems , vol. 23, pp. 1324–1332, 2010
2010
Earlier work this paper cites.
H. Idrees, I. Saleemi, C. Seibert, and M. Shah, “Multi-source multi-scale counting in extremely dense crowd images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2013, pp. 2547–2554
2013
Earlier work this paper cites.
V. Mnih, N. Heess, A. Graves et al. , “Recurrent models of visual attention,” in Advances in Neural Information Processing Systems , 2014, pp. 2204–2212
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
B. Liu and N. Vasconcelos, “Bayesian model adaptation for crowd counts,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 4175–4183
2015
Earlier work this paper cites.
C. Wang, H. Zhang, L. Yang, S. Liu, and X. Cao, “Deep people counting in extremely dense crowds,” in Proceedings of the 23rd ACM international conference on Multimedia , 2015, pp. 1299–1302
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Conference on Learning Representations , May 2015
2015
Earlier work this paper cites.
C. Shang, H. Ai, and B. Bai, “End-to-end crowd counting via joint learning local and global count,” in 2016 IEEE International Conference on Image Processing . IEEE, 2016, pp. 1215–1219
2016
Earlier work this paper cites.
Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, “Single-image crowd counting via multi-column convolutional neural network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , June 2016
2016
Earlier work this paper cites.
S. Zhang, G. Wu, J. P. Costeira, and J. M. Moura, “Fcn-rlstm: Deep spatio-temporal neural networks for vehicle counting in city cameras,” in Proceedings of the IEEE International Conference on Computer Vision , 2017
2017
Earlier work this paper cites.
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2017, pp. 764–773
2017
Earlier work this paper cites.
P. Chattopadhyay, R. Vedantam, R. R. Selvaraju, D. Batra, and D. Parikh, “Counting everyday objects in everyday scenes,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 1135–1144
2017
Earlier work this paper cites.
V. A. Sindagi and V. M. Patel, “Generating high-quality crowd density maps using contextual pyramid cnns,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 1861–1870
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 1251–1258
2017
Earlier work this paper cites.
T. Wang, M. Qiao, Z. Lin, C. Li, H. Snoussi, Z. Liu, and C. Choi, “Generative neural networks for anomaly detection in crowded scenes,” IEEE Transactions on Information Forensics and Security , vol. 14, no. 5, pp. 1390–1399, 2018
2018
Earlier work this paper cites.
Y. Li, X. Zhang, and D. Chen, “Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 1091–1100
2018
Earlier work this paper cites.
X. Liu, S. W. Chen, S. Aditya, N. Sivakumar, S. Dcunha, C. Qu, C. J. Taylor, J. Das, and V. Kumar, “Robust fruit counting: Combining deep learning, tracking, and structure from motion,” in IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2018, pp. 1045–1052
2018
Earlier work this paper cites.
M. Marsden, K. McGuinness, S. Little, C. E. Keogh, and N. E. O’Connor, “People, penguins and petri dishes: Adapting object counting models to new visual domains and object types without forgetting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018
2018
Earlier work this paper cites.
Z. Zou, X. Su, X. Qu, and P. Zhou, “Da-net: Learning the fine-grained density distribution with deformation aggregation network,” IEEE Access , vol. 6, pp. 60 745–60 756, 2018
2018
Earlier work this paper cites.
H. Idrees, M. Tayyab, K. Athrey, D. Zhang, S. Al-Maadeed, N. Rajpoot, and M. Shah, “Composition loss for counting, density map estimation and localization in dense crowds,” in Proceedings of the European Conference on Computer Vision , 2018, pp. 532–546
2018
Cited alongside, same era.
Y. Li, X. Zhang, and D. Chen, “Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , June 2018
2018
Cited alongside, same era.
X. Cao, Z. Wang, Y. Zhao, and F. Su, “Scale aggregation network for accurate and efficient crowd counting,” in Proceedings of the European Conference on Computer Vision , 2018, pp. 734–750
2018
Cited alongside, same era.
Z. Ma, X. Wei, X. Hong, and Y. Gong, “Bayesian loss for crowd count estimation with point supervision,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 6142–6151
2019
Cited alongside, same era.
J. Wan and A. Chan, “Modeling noisy annotations for crowd counting,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Later among the works it cites.
Q. Wang, J. Gao, W. Lin, and X. Li, “Nwpu-crowd: A large-scale benchmark for crowd counting and localization,” IEEE transactions on pattern analysis and machine intelligence , vol. 43, no. 6, pp. 2141–2149, 2020
2020
Later among the works it cites.
V. Sindagi, R. Yasarla, and V. M. Patel, “Jhu-crowd++: Large-scale crowd counting dataset and a benchmark method,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
Later among the works it cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2021
2021
Closest in time.
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N. Liu, Y. Long, C. Zou, Q. Niu, L. Pan, and H. Wu, “Adcrowdnet: An attention-injective deformable convolutional network for crowd understanding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , June 2019
2019
Cited alongside, same era.
D. Guo, K. Li, Z.-J. Zha, and M. Wang, “Dadnet: Dilated-attention-deformable convnet for crowd counting,” in Proceedings of the 27th ACM International Conference on Multimedia , 2019, pp. 1823–1832
2019
Cited alongside, same era.
W. Liu, M. Salzmann, and P. Fua, “Context-aware crowd counting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , June 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
W. Liu, M. Salzmann, and P. Fua, “Context-aware crowd counting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 5099–5108
2019
Cited alongside, same era.
Q. Wang, J. Gao, W. Lin, and Y. Yuan, “Learning from synthetic data for crowd counting in the wild,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8198–8207
2019
Cited alongside, same era.
M. Shi, Z. Yang, C. Xu, and Q. Chen, “Revisiting perspective information for efficient crowd counting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 7279–7288
2019
Cited alongside, same era.
H. Xiong, H. Lu, C. Liu, L. Liu, Z. Cao, and C. Shen, “From open set to closed set: Counting objects by spatial divide-and-conquer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 8362–8371
2019
Cited alongside, same era.
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jegou, “Training data-efficient image transformers & distillation through attention,” in Proceedings of the 38th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, vol. 139. PMLR, 18–24 Jul 2021, pp. 10 347–10 357
2021
Closest in time.
L. Yuan, Y. Chen, T. Wang, W. Yu, Y. Shi, Z.-H. Jiang, F. E. Tay, J. Feng, and S. Yan, “Tokens-to-token vit: Training vision transformers from scratch on imagenet,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , October 2021, pp. 558–567
2021
Closest in time.
S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P. H. Torr, and L. Zhang, “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , June 2021, pp. 6881–6890
2021
Closest in time.
X. Liu, G. Li, Z. Han, W. Zhang, Y. Yang, Q. Huang, and N. Sebe, “Exploiting sample correlation for crowd counting with multi-expert network,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , October 2021, pp. 3215–3224
2021
Closest in time.
X. Chu, Z. Tian, Y. Wang, B. Zhang, H. Ren, X. Wei, H. Xia, and C. Shen, “Twins: Revisiting the design of spatial attention in vision transformers,” in Advances in Neural Information Processing Systems , 2021
2021
Closest in time.
Z. Ma, X. Wei, X. Hong, H. Lin, Y. Qiu, and Y. Gong, “Learning to count via unbalanced optimal transport,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 3, 2021, pp. 2319–2327
2021
Closest in time.
Q. Song, C. Wang, Y. Wang, Y. Tai, C. Wang, J. Li, J. Wu, and J. Ma, “To choose or to fuse? scale selection for crowd counting,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 3, 2021, pp. 2576–2583
2021
Closest in time.
Z. Yan, R. Zhang, H. Zhang, Q. Zhang, and W. Zuo, “Crowd counting via perspective-guided fractional-dilation convolution,” IEEE Transactions on Multimedia , 2021
2021
Closest in time.
L. Rong and C. Li, “Coarse-and fine-grained attention network with background-aware loss for crowd density map estimation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 3675–3684
2021
Closest in time.
H. Lin, X. Hong, Z. Ma, X. Wei, Y. Qiu, Y. Wang, and Y. Gong, “Direct measure matching for crowd counting,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 , Z.-H. Zhou, Ed. International Joint Conferences on Artificial Intelligence Organization, 8 2021, pp. 837–844
2021
Closest in time.
Y. Wang, J. Hou, X. Hou, and L.-P. Chau, “A self-training approach for point-supervised object detection and counting in crowds,” IEEE Transactions on Image Processing , vol. 30, pp. 2876–2887, 2021
2021
Closest in time.
Z. Ma, X. Hong, X. Wei, Y. Qiu, and Y. Gong, “Towards a universal model for cross-dataset crowd counting,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3205–3214
2021
Closest in time.
B. Chen, Z. Yan, K. Li, P. Li, B. Wang, W. Zuo, and L. Zhang, “Variational attention: Propagating domain-specific knowledge for multi-domain learning in crowd counting,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 065–16 075
2021
Closest in time.
G. Sun, Y. Liu, T. Probst, D. P. Paudel, N. Popovic, and L. V. Gool, “Boosting crowd counting with transformers,” 2021
2021
Closest in time.
Y. Tian, X. Chu, and H. Wang, “Cctrans: Simplifying and improving crowd counting with transformer,” 2021
2021
Closest in time.
W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021
2021
Closest in time.
A. El-Nouby, N. Neverova, I. Laptev, and H. Jégou, “Training vision transformers for image retrieval,” 2021
2021
Closest in time.
J. Cheng, H. Xiong, Z. Cao, and H. Lu, “Decoupled two-stage crowd counting and beyond,” IEEE Transactions on Image Processing , vol. 30, pp. 2862–2875, 2021
2021
Closest in time.
Q. Song, C. Wang, Z. Jiang, Y. Wang, Y. Tai, C. Wang, J. Li, F. Huang, and Y. Wu, “Rethinking counting and localization in crowds: A purely point-based framework,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3365–3374
2021
Closest in time.
Q. Wang, J. Gao, W. Lin, and X. Li, “Nwpu-crowd: A large-scale benchmark for crowd counting and localization,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, no. 6, pp. 2141–2149, 2021
2021
Closest in time.
K. Wu, H. Peng, M. Chen, J. Fu, and H. Chao, “Rethinking and improving relative position encoding for vision transformer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 10 033–10 041
2021
Closest in time.
S. Aich and I. Stavness, “Leaf counting with deep convolutional and deconvolutional networks,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2017, pp. 2080–2089
2089
Closest in time.