Modality-missing RGBT Tracking: Invertible Prompt Learning and High-quality Benchmarks

TL;DR

This paper introduces an invertible prompt learning approach for robust RGBT tracking under modality-missing scenarios, supported by high-quality benchmarks and extensive experiments demonstrating significant performance improvements.

Contribution

The paper proposes a novel invertible prompt learning method to handle modality-missing in RGBT tracking, along with the creation of comprehensive benchmark datasets for evaluation.

Findings

Significant performance improvements over state-of-the-art methods.

Effective handling of modality-missing scenarios in RGBT tracking.

Introduction of high-quality benchmark datasets for real-world challenges.

Abstract

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}.