  Visible-modal object tracking gives rise to a series of downstream
multi-modal tracking tributaries. To inherit the powerful representations of
the foundation model, a natural modus operandi for multi-modal tracking is full
fine-tuning on the RGB-based parameters. Albeit effective, this manner is not
optimal due to the scarcity of downstream data and poor transferability, etc.
In this paper, inspired by the recent success of the prompt learning in
language models, we develop Visual Prompt multi-modal Tracking (ViPT), which
learns the modal-relevant prompts to adapt the frozen pre-trained foundation
model to various downstream multimodal tracking tasks. ViPT finds a better way
to stimulate the knowledge of the RGB-based model that is pre-trained at scale,
meanwhile only introducing a few trainable parameters (less than 1% of model
parameters). ViPT outperforms the full fine-tuning paradigm on multiple
downstream tracking tasks including RGB+Depth, RGB+Thermal, and RGB+Event
tracking. Extensive experiments show the potential of visual prompt learning
for multi-modal tracking, and ViPT can achieve state-of-the-art performance
while satisfying parameter efficiency. Code and models are available at
https://github.com/jiawen-zhu/ViPT.
