  Multi-modal tracking gains attention due to its ability to be more accurate
and robust in complex scenarios compared to traditional RGB-based tracking. Its
key lies in how to fuse multi-modal data and reduce the gap between modalities.
However, multi-modal tracking still severely suffers from data deficiency, thus
resulting in the insufficient learning of fusion modules. Instead of building
such a fusion module, in this paper, we provide a new perspective on
multi-modal tracking by attaching importance to the multi-modal visual prompts.
We design a novel multi-modal prompt tracker (ProTrack), which can transfer the
multi-modal inputs to a single modality by the prompt paradigm. By best
employing the tracking ability of pre-trained RGB trackers learning at scale,
our ProTrack can achieve high-performance multi-modal tracking by only altering
the inputs, even without any extra training on multi-modal data. Extensive
experiments on 5 benchmark datasets demonstrate the effectiveness of the
proposed ProTrack.
