  In crowd counting, due to the problem of laborious labelling, it is perceived
intractability of collecting a new large-scale dataset which has plentiful
images with large diversity in density, scene, etc. Thus, for learning a
general model, training with data from multiple different datasets might be a
remedy and be of great value. In this paper, we resort to the multi-domain
joint learning and propose a simple but effective Domain-specific Knowledge
Propagating Network (DKPNet)1 for unbiasedly learning the knowledge from
multiple diverse data domains at the same time. It is mainly achieved by
proposing the novel Variational Attention(VA) technique for explicitly modeling
the attention distributions for different domains. And as an extension to VA,
Intrinsic Variational Attention(InVA) is proposed to handle the problems of
over-lapped domains and sub-domains. Extensive experiments have been conducted
to validate the superiority of our DKPNet over several popular datasets,
including ShanghaiTech A/B, UCF-QNRF and NWPU.
