  Crowd counting research has made significant advancements in real-world
applications, but it remains a formidable challenge in cross-modal settings.
Most existing methods rely solely on the optical features of RGB images,
ignoring the feasibility of other modalities such as thermal and depth images.
The inherently significant differences between the different modalities and the
diversity of design choices for model architectures make cross-modal crowd
counting more challenging. In this paper, we propose Cross-modal Spatio-Channel
Attention (CSCA) blocks, which can be easily integrated into any
modality-specific architecture. The CSCA blocks first spatially capture global
functional correlations among multi-modality with less overhead through
spatial-wise cross-modal attention. Cross-modal features with spatial attention
are subsequently refined through adaptive channel-wise feature aggregation. In
our experiments, the proposed block consistently shows significant performance
improvement across various backbone networks, resulting in state-of-the-art
results in RGB-T and RGB-D crowd counting.
