Adversarial scale-adaptive neural network for crowd counting

Zhang, 2016, Single-image crowd counting via multi-column convolutional neural network, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Babu Sam, 2017, Switching convolutional neural network for crowd counting, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) V.A. Sindagi, V.M. Patel, Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting, in: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2017. doi: 10.1109/avss.2017.8078491. Zhang, 2018, Crowd counting via scale-adaptive convolutional neural network, IEEE Winter Conference on Applications of Computer Vision Li, 2018, Csrnet: dilated convolutional neural networks for understanding the highly congested scenes, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 10.1007/978-3-030-03398-9 K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, Comput. Intell. Neurosci. (2014) Shi, 2016, Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Idrees, 2013, Multi-source multi-scale counting in extremely dense crowd images, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Idrees, 2018, Composition loss for counting, density map estimation and localization in dense crowds, The European Conference on Computer Vision (ECCV) Cong, 2015, Cross-scene crowd counting via deep convolutional neural networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Viola, 2004, Robust real-time face detection, Int. J. Comput. Vision, 57, 137, 10.1023/B:VISI.0000013087.49260.fb N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, IEEE, 2005, pp. 886–893 A. Bansal, K.S. Venkatesh, People counting in high density crowds from still images, CoRR abs/1507.08445 (2015) A.B. Chan, Z.-S.J. Liang, N. Vasconcelos, Privacy preserving crowd monitoring: counting people without people models or tracking, in: 2008 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2008, pp. 1–7 Chen, 2013, Cumulative attribute space for age and crowd density estimation, in, 2467 Walach, 2016, Learning to count with cnn boosting, The European Conference on Computer Vision (ECCV) Chong, 2016, End-to-end crowd counting via joint learning local and global count, IEEE International Conference on Image Processing (ICIP) Gao, 2019, Pcc net: perspective crowd counting via spatial convolutional network, IEEE Trans. Circuits Syst. Video Technol. Guo, 2019, Dadnet: dilated-attention-deformable convnet for crowd counting, 1823 Chen, 2020, Relevant region prediction for crowd counting, Neurocomputing, 10.1016/j.neucom.2020.04.117 Chen, 2020, Crowd counting with crowd attention convolutional neural network, Neurocomputing, 382, 210, 10.1016/j.neucom.2019.11.064 Chen, 2018, Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE Trans. Pattern Anal. Mach. Intell., 40, 834, 10.1109/TPAMI.2017.2699184 F. Yu, V. Koltun, Multi-scale context aggregation by dilated convolutions, arXiv:1511.07122 (2015). L.-C. Chen, G. Papandreou, F. Schroff, H. Adam, Rethinking atrous convolution for semantic image segmentation, arXiv preprint arXiv:1706.05587 (2017a). L. Chen, G. Papandreou, F. Schroff, H. Adam, Rethinking atrous convolution for semantic image segmentation, CoRR abs/1706.05587 (2017b). Wang, 2018, Understanding convolution for semantic segmentation, IEEE Winter Conference on Applications of Computer Vision Tan, 2020, Efficientdet: scalable and efficient object detection, 10781