  Object tracking based on the fusion of visible and thermal im-ages, known as
RGB-T tracking, has gained increasing atten-tion from researchers in recent
years. How to achieve a more comprehensive fusion of information from the two
modalities with fewer computational costs has been a problem that re-searchers
have been exploring. Recently, with the rise of prompt learning in computer
vision, we can better transfer knowledge from visual large models to downstream
tasks. Considering the strong complementarity between visible and thermal
modalities, we propose a tracking architecture based on mutual prompt learning
between the two modalities. We also design a lightweight prompter that
incorporates attention mechanisms in two dimensions to transfer information
from one modality to the other with lower computational costs, embedding it
into each layer of the backbone. Extensive ex-periments have demonstrated that
our proposed tracking ar-chitecture is effective and efficient, achieving
state-of-the-art performance while maintaining high running speeds.
