  The mainstream crowd counting methods usually utilize the convolution neural
network (CNN) to regress a density map, requiring point-level annotations.
However, annotating each person with a point is an expensive and laborious
process. During the testing phase, the point-level annotations are not
considered to evaluate the counting accuracy, which means the point-level
annotations are redundant. Hence, it is desirable to develop weakly-supervised
counting methods that just rely on count level annotations, a more economical
way of labeling. Current weakly-supervised counting methods adopt the CNN to
regress a total count of the crowd by an image-to-count paradigm. However,
having limited receptive fields for context modeling is an intrinsic limitation
of these weakly-supervised CNN-based methods. These methods thus can not
achieve satisfactory performance, limited applications in the real-word. The
Transformer is a popular sequence-to-sequence prediction model in NLP, which
contains a global receptive field. In this paper, we propose TransCrowd, which
reformulates the weakly-supervised crowd counting problem from the perspective
of sequence-to-count based on Transformer. We observe that the proposed
TransCrowd can effectively extract the semantic crowd information by using the
self-attention mechanism of Transformer. To the best of our knowledge, this is
the first work to adopt a pure Transformer for crowd counting research.
Experiments on five benchmark datasets demonstrate that the proposed TransCrowd
achieves superior performance compared with all the weakly-supervised CNN-based
counting methods and gains highly competitive counting performance compared
with some popular fully-supervised counting methods. Code is available at
https://github.com/dk-liang/TransCrowd.
