  Multimodal visual information fusion aims to integrate the multi-sensor data
into a single image which contains more complementary information and less
redundant features. However the complementary information is hard to extract,
especially for infrared and visible images which contain big similarity gap
between these two modalities. The common cross attention modules only consider
the correlation, on the contrary, image fusion tasks need focus on
complementarity (uncorrelation). Hence, in this paper, a novel cross attention
mechanism (CAM) is proposed to enhance the complementary information.
Furthermore, a two-stage training strategy based fusion scheme is presented to
generate the fused images. For the first stage, two auto-encoder networks with
same architecture are trained for each modality. Then, with the fixed encoders,
the CAM and a decoder are trained in the second stage. With the trained CAM,
features extracted from two modalities are integrated into one fused feature in
which the complementary information is enhanced and the redundant features are
reduced. Finally, the fused image can be generated by the trained decoder. The
experimental results illustrate that our proposed fusion method obtains the
SOTA fusion performance compared with the existing fusion networks. The codes
are available at https://github.com/hli1221/CrossFuse
