  Currently, for crowd counting, the fully supervised methods via density map
estimation are the mainstream research directions. However, such methods need
location-level annotation of persons in an image, which is time-consuming and
laborious. Therefore, the weakly supervised method just relying upon the
count-level annotation is urgently needed. Since CNN is not suitable for
modeling the global context and the interactions between image patches, crowd
counting with weakly supervised learning via CNN generally can not show good
performance. The weakly supervised model via Transformer was sequentially
proposed to model the global context and learn contrast features. However, the
transformer directly partitions the crowd images into a series of tokens, which
may not be a good choice due to each pedestrian being an independent
individual, and the parameter number of the network is very large. Hence, we
propose a Joint CNN and Transformer Network (JCTNet) via weakly supervised
learning for crowd counting in this paper. JCTNet consists of three parts: CNN
feature extraction module (CFM), Transformer feature extraction module (TFM),
and counting regression module (CRM). In particular, the CFM extracts crowd
semantic information features, then sends their patch partitions to TRM for
modeling global context, and CRM is used to predict the number of people.
Extensive experiments and visualizations demonstrate that JCTNet can
effectively focus on the crowd regions and obtain superior weakly supervised
counting performance on five mainstream datasets. The number of parameters of
the model can be reduced by about 67%~73% compared with the pure Transformer
works. We also tried to explain the phenomenon that a model constrained only by
count-level annotations can still focus on the crowd regions. We believe our
work can promote further research in this field.
