  Object counting aims to estimate the number of objects in images. The leading
counting approaches focus on the single category counting task and achieve
impressive performance. Note that there are multiple categories of objects in
real scenes. Multi-class object counting expands the scope of application of
object counting task. The multi-target detection task can achieve multi-class
object counting in some scenarios. However, it requires the dataset annotated
with bounding boxes. Compared with the point annotations in mainstream object
counting issues, the coordinate box-level annotations are more difficult to
obtain. In this paper, we propose a simple yet efficient counting network based
on point-level annotations. Specifically, we first change the traditional
output channel from one to the number of categories to achieve multiclass
counting. Since all categories of objects use the same feature extractor in our
proposed framework, their features will interfere mutually in the shared
feature space. We further design a multi-mask structure to suppress harmful
interaction among objects. Extensive experiments on the challenging benchmarks
illustrate that the proposed method achieves state-of-the-art counting
performance.
