  Infrared-visible image fusion (IVIF) has attracted much attention owing to
the highly-complementary properties of the two image modalities. Due to the
lack of ground-truth fused images, the fusion output of current deep-learning
based methods heavily depends on the loss functions defined mathematically. As
it is hard to well mathematically define the fused image without ground truth,
the performance of existing fusion methods is limited. In this paper, we first
propose to use natural language to express the objective of IVIF, which can
avoid the explicit mathematical modeling of fusion output in current losses,
and make full use of the advantage of language expression to improve the fusion
performance. For this purpose, we present a comprehensive language-expressed
fusion objective, and encode relevant texts into the multi-modal embedding
space using CLIP. A language-driven fusion model is then constructed in the
embedding space, by establishing the relationship among the embedded vectors to
represent the fusion objective and input image modalities. Finally, a
language-driven loss is derived to make the actual IVIF aligned with the
embedded language-driven fusion model via supervised training. Experiments show
that our method can obtain much better fusion results than existing techniques.
