  This paper investigates the role of global context for crowd counting.
Specifically, a pure transformer is used to extract features with global
information from overlapping image patches. Inspired by classification, we add
a context token to the input sequence, to facilitate information exchange with
tokens corresponding to image patches throughout transformer layers. Due to the
fact that transformers do not explicitly model the tried-and-true channel-wise
interactions, we propose a token-attention module (TAM) to recalibrate encoded
features through channel-wise attention informed by the context token. Beyond
that, it is adopted to predict the total person count of the image through
regression-token module (RTM). Extensive experiments on various datasets,
including ShanghaiTech, UCF-QNRF, JHU-CROWD++ and NWPU, demonstrate that the
proposed context extraction techniques can significantly improve the
performance over the baselines.
