Boosting Crowd Counting via Multifaceted Attention

Hui Lin, Zhiheng Ma, Rongrong Ji, Yaowei Wang, Xiaopeng Hong; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 19628-19637

This paper focuses on crowd counting. As large-scale variations often exist within crowd images, neither fixed-size convolution kernel of CNN nor fixed-size attentions of recent vision transformers can well handle this kind of variations. To address this problem, we propose a Multifaceted Attention Network (MAN), which incorporates global attention from vanilla transformer, learnable local attention, attention regularization and instance attention into a counting model. Firstly, the local Learnable Region Attention (LRA) is proposed to assign attention exclusive for each feature location dynamically. Secondly, we design the Local Attention Regularization to supervise the training of LRA by minimizing the deviation among the attention for different feature locations. Finally, we provide an Instance Attention mechanism to focus on the most important instances dynamically during training. Extensive experiments on four challenging crowd counting datasets namely ShanghaiTech, UCF-QNRF, JHU++, and NWPU have validated the proposed method.