  The existing generative adversarial fusion methods generally concatenate
source images and extract local features through convolution operation, without
considering their global characteristics, which tends to produce an unbalanced
result and is biased towards the infrared image or visible image. Toward this
end, we propose a novel end-to-end mode based on generative adversarial
training to achieve better fusion balance, termed as \textit{interactive
compensatory attention fusion network} (ICAFusion). In particular, in the
generator, we construct a multi-level encoder-decoder network with a triple
path, and adopt infrared and visible paths to provide additional intensity and
gradient information. Moreover, we develop interactive and compensatory
attention modules to communicate their pathwise information, and model their
long-range dependencies to generate attention maps, which can more focus on
infrared target perception and visible detail characterization, and further
increase the representation power for feature extraction and feature
reconstruction. In addition, dual discriminators are designed to identify the
similar distribution between fused result and source images, and the generator
is optimized to produce a more balanced result. Extensive experiments
illustrate that our ICAFusion obtains superior fusion performance and better
generalization ability, which precedes other advanced methods in the subjective
visual description and objective metric evaluation. Our codes will be public at
\url{https://github.com/Zhishe-Wang/ICAFusion}
