  Due to the rapid development of computer vision, single-modal (RGB) object
tracking has made significant progress in recent years. Considering the
limitation of single imaging sensor, multi-modal images (RGB, Infrared, etc.)
are introduced to compensate for this deficiency for all-weather object
tracking in complex environments. However, as acquiring sufficient multi-modal
tracking data is hard while the dominant modality changes with the open
environment, most existing techniques fail to extract multi-modal complementary
information dynamically, yielding unsatisfactory tracking performance. To
handle this problem, we propose a novel multi-modal visual prompt tracking
model based on a universal bi-directional adapter, cross-prompting multiple
modalities mutually. Our model consists of a universal bi-directional adapter
and multiple modality-specific transformer encoder branches with sharing
parameters. The encoders extract features of each modality separately by using
a frozen pre-trained foundation model. We develop a simple but effective light
feature adapter to transfer modality-specific information from one modality to
another, performing visual feature prompt fusion in an adaptive manner. With
adding fewer (0.32M) trainable parameters, our model achieves superior tracking
performance in comparison with both the full fine-tuning methods and the prompt
learning-based methods. Our code is available:
https://github.com/SparkTempest/BAT.
