Prompting for Multi-Modal Tracking

• URL: http://arxiv.org/abs/2207.14571v2
• Date: Mon, 1 Aug 2022 02:50:52 GMT
• Title: Prompting for Multi-Modal Tracking
• Authors: Jinyu Yang and Zhe Li and Feng Zheng and Ale\v{s} Leonardis and Jingkuan Song
• Abstract summary: We propose a novel multi-modal prompt tracker (ProTrack) for multi-modal tracking. ProTrack can transfer the multi-modal inputs to a single modality by the prompt paradigm. Our ProTrack can achieve high-performance multi-modal tracking by only altering the inputs, even without any extra training on multi-modal data.
• Score: 70.0522146292258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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