  This paper focuses on the challenging crowd counting task. As large-scale
variations often exist within crowd images, neither fixed-size convolution
kernel of CNN nor fixed-size attention of recent vision transformers can well
handle this kind of variation. To address this problem, we propose a
Multifaceted Attention Network (MAN) to improve transformer models in local
spatial relation encoding. MAN incorporates global attention from a vanilla
transformer, learnable local attention, and instance attention into a counting
model. Firstly, the local Learnable Region Attention (LRA) is proposed to
assign attention exclusively 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. Codes:
https://github.com/LoraLinH/Boosting-Crowd-Counting-via-Multifaceted-Attention.
