Current RGBT tracking researches mainly focus on the modality-complete
scenarios, overlooking the modality-missing challenge in real-world scenes. In
this work, we comprehensively investigate the impact of modality-missing
challenge in RGBT tracking and propose a novel invertible prompt learning
approach, which integrates the content-preserving prompts into a well-trained
tracking model to adapt to various modality-missing scenarios, for
modality-missing RGBT tracking. In particular, given one modality-missing
scenario, we propose to utilize the available modality to generate the prompt
of the missing modality to adapt to RGBT tracking model. However, the
cross-modality gap between available and missing modalities usually causes
semantic distortion and information loss in prompt generation. To handle this
issue, we propose the invertible prompt learning scheme by incorporating the
full reconstruction of the input available modality from the prompt in prompt
generation model. Considering that there lacks a modality-missing RGBT tracking
dataset and many modality-missing scenarios are difficult to capture, we design
a high-quality data simulation method based on hierarchical combination schemes
to generate real-world modality-missing data. Extensive experiments on three
modality-missing datasets show that our method achieves significant performance
improvements compared with state-of-the-art methods. We will release the code
and simulation dataset.

在当前 RGBT 跟踪研究中，主要关注完整模态的场景，忽视了现实场景中模态缺失的挑战。本文综合研究了模态缺失挑战对 RGBT 跟踪的影响，并提出了一种新的可逆提示学习方法，将保留内容的提示集成到训练充分的跟踪模型中，以适应各种模态缺失场景，用于模态缺失的 RGBT 跟踪。