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arXiv · 2207.14571

Prompting for Multi-Modal Tracking

Jinyu Yang·Zhe Li·Feng Zheng·Aleš Leonardis·Jingkuan Song
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Abstract

Multi-modal tracking gains attention due to its ability to be more accurate and robust in complex scenarios compared to traditional RGB-based tracking. Its key lies in how to fuse multi-modal data and reduce the gap between modalities. However, multi-modal tracking still severely suffers from data deficiency, thus resulting in the insufficient learning of fusion modules. Instead of building such a fusion module, in this paper, we provide a new perspective on multi-modal tracking by attaching importance to the multi-modal visual prompts. We design a novel multi-modal prompt tracker (ProTrack), which can transfer the multi-modal inputs to a single modality by the prompt paradigm. By best employing the tracking ability of pre-trained RGB trackers learning at scale, our ProTrack can achieve high-performance multi-modal tracking by only altering the inputs, even without any extra training on multi-modal data. Extensive experiments on 5 benchmark datasets demonstrate the effectiveness of the proposed ProTrack.

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Jinyu Yang, Zhe Li, Feng Zheng, Aleš Leonardis, Jingkuan Song. 2022-07-29. Prompting for Multi-Modal Tracking. https://arxiv.org/abs/2207.14571

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