  In recent years, significant progress has been made on the research of crowd
counting. However, as the challenging scale variations and complex scenes
existed in crowds, neither traditional convolution networks nor recent
Transformer architectures with fixed-size attention could handle the task well.
To address this problem, this paper proposes a scene-adaptive attention
network, termed SAANet. First of all, we design a deformable attention in-built
Transformer backbone, which learns adaptive feature representations with
deformable sampling locations and dynamic attention weights. Then we propose
the multi-level feature fusion and count-attentive feature enhancement modules
further, to strengthen feature representation under the global image context.
The learned representations could attend to the foreground and are adaptive to
different scales of crowds. We conduct extensive experiments on four
challenging crowd counting benchmarks, demonstrating that our method achieves
state-of-the-art performance. Especially, our method currently ranks No.1 on
the public leaderboard of the NWPU-Crowd benchmark. We hope our method could be
a strong baseline to support future research in crowd counting. The source code
will be released to the community.
