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

arXiv:2312.16244v3 Announce Type: replace
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 …

arxiv benchmarks cs.cv prompt prompt learning quality tracking type