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.

基于可见光和热红外图像融合的目标跟踪，称为 RGB-T 跟踪，在计算成本较低的情况下如何实现两种模态信息更全面的融合一直是研究人员探索的问题。最近，随着计算机视觉中即时学习的兴起，我们可以更好地将知识从视觉大模型转移到下游任务中。考虑到可见光和热红外模态之间的强互补性，我们提出了一种基于两种模态之间相互即时学习的跟踪架构。我们还设计了一个轻量级的即时学习器，在骨干网络的每一层中嵌入了二维注意机制，以低计算成本将信息从一种模态转移到另一种模态中。广泛的实验证明，我们提出的跟踪架构既有效又高效，在保持高运行速度的同时实现了最先进的性能。