  In real-world crowd counting applications, the crowd densities in an image
vary greatly. When facing density variation, humans tend to locate and count
the targets in low-density regions, and reason the number in high-density
regions. We observe that CNN focus on the local information correlation using a
fixed-size convolution kernel and the Transformer could effectively extract the
semantic crowd information by using the global self-attention mechanism. Thus,
CNN could locate and estimate crowds accurately in low-density regions, while
it is hard to properly perceive the densities in high-density regions. On the
contrary, Transformer has a high reliability in high-density regions, but fails
to locate the targets in sparse regions. Neither CNN nor Transformer can well
deal with this kind of density variation. To address this problem, we propose a
CNN and Transformer Adaptive Selection Network (CTASNet) which can adaptively
select the appropriate counting branch for different density regions. Firstly,
CTASNet generates the prediction results of CNN and Transformer. Then,
considering that CNN/Transformer is appropriate for low/high-density regions, a
density guided adaptive selection module is designed to automatically combine
the predictions of CNN and Transformer. Moreover, to reduce the influences of
annotation noise, we introduce a Correntropy based optimal transport loss.
Extensive experiments on four challenging crowd counting datasets have
validated the proposed method.
