  Current RGBT tracking research relies on the complete multi-modal input, but
modal information might miss due to some factors such as thermal sensor
self-calibration and data transmission error, called modality-missing challenge
in this work. To address this challenge, we 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
robust RGBT tracking. 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 design the
invertible prompter by incorporating the full reconstruction of the input
available modality from the generated prompt. To provide a comprehensive
evaluation platform, we construct several high-quality benchmark datasets, in
which various modality-missing scenarios are considered to simulate real-world
challenges. Extensive experiments on three modality-missing benchmark datasets
show that our method achieves significant performance improvements compared
with state-of-the-art methods. We have released the code and simulation
datasets at:
\href{https://github.com/Alexadlu/Modality-missing-RGBT-Tracking.git}{https://github.com/Alexadlu/Modality-missing-RGBT-Tracking.git}.
