  Most recent methods used for crowd counting are based on the convolutional
neural network (CNN), which has a strong ability to extract local features. But
CNN inherently fails in modeling the global context due to the limited
receptive fields. However, the transformer can model the global context easily.
In this paper, we propose a simple approach called CCTrans to simplify the
design pipeline. Specifically, we utilize a pyramid vision transformer backbone
to capture the global crowd information, a pyramid feature aggregation (PFA)
model to combine low-level and high-level features, an efficient regression
head with multi-scale dilated convolution (MDC) to predict density maps.
Besides, we tailor the loss functions for our pipeline. Without bells and
whistles, extensive experiments demonstrate that our method achieves new
state-of-the-art results on several benchmarks both in weakly and
fully-supervised crowd counting. Moreover, we currently rank No.1 on the
leaderboard of NWPU-Crowd. Our code will be made available.
