# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 2 Related Work
### CNN-Based Methods  
With the rapid development of the convolutional neural network, the brilliance in computer vision involved crowd counting was shown.  
In 2015, [PERSON] et al. [24] trained CNNs to regress the crowd density map. They retrieved images using density and perspective information, then used them to fine-tune the trained network and predicted the density map. However, its applicability was limited by requirements of perspective maps and the fine-tuning of each test scene. In 2016, a multi-scale CNN architecture was used by [PERSON] et al. [16] to tackle the large scale variations in crowd scenes. And fully-connected layers were used to fuse the maps from each of the CNN trained at a particular scale to regress the density map. It aimed to solve the trouble in scale variation. Then, the multi-column [8; 25] or multi-scale [3; 7; 12] network architecture was often utilized for crowd density regression. To be specific, [PERSON] et al. [8] introduced a classifier to choose a specified column for training according to dense levels. [PERSON] et al. [7] used a scale aggregation module as an encoder to extract features in diverse scales, and a group of transposed convolutions as a decoder to generate high-quality density maps. The local pattern consistency loss was as well proposed. [PERSON] et al. [12] combined the feature maps of multiple layers to adapt the changes in pedestrian scale and perspective and introduced a multi-task loss by adding a relative head count loss. But some works [11; 12; 13] suggested to replace diverse convolutional kernels with piles of the same size kernels. [PERSON] et al. [11] verified that the effectiveness of using multi-columns may not be prominent and that each column in such a branch structure had learned nearly identical features. They used VGG16 as the baseline and introduced dilated layers to the backend, which was a great improvement.  
Furthermore, extra information was added to the training process to remedy background interference [14; 15]. [PERSON] et al. [14] proposed a self-supervised task to optimize the training of networks for crowd counting, leveraging unlabeled crowd images at training time to significantly improve performance. This generated a ranking of sub-images which were used to train a network to estimate whether one image contained more persons than another. [PERSON] et al. [15] integrated the perspective information into crowd density maps for efficient crowd counting to provide additional information about the person scale change in an image. It was particularly helpful on the density regression of a small person area. However, extra information and tasks may require more resources and computations.  
In 2019, more solutions have been proposed to solve this problem. [PERSON] et al. [26] constructed a large-scale and diverse synthetic crowd counting dataset to pretrain their designed Spatial Fully Convolutional Network. [PERSON] et al. [27] introduced an end-to-end architecture that combined features obtained using multiple receptive field sizes and learned the importance of each such feature at each image location. [PERSON] et al. [28] detected ambiguous image regions to zoom into high resolution for re-inspection, and added the localization task. Almost all the methods added extra information or tasks for enhancing the single counting task.