中文版 | English
Title

Prompting for Multi-Modal Tracking

Author
Corresponding AuthorFeng Zheng
DOI
Publication Years
2022-08-01
Conference Name
The 30th ACM International Conference on Multimedia
Conference Date
2022/10/10-2022/10/14
Conference Place
里斯本
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 multimodal 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 pretrained 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.

SUSTech Authorship
First ; Corresponding
Language
English
Data Source
人工提交
Publication Status
在线出版
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/415620
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.Southern University of Science and Technology, China
2.University of Birmingham, UK
3.University of Electronic Science and Technology of China
First Author AffilicationSouthern University of Science and Technology
Corresponding Author AffilicationSouthern University of Science and Technology
First Author's First AffilicationSouthern University of Science and Technology
Recommended Citation
GB/T 7714
Jinyu Yang,Zhe Li,Feng Zheng,et al. Prompting for Multi-Modal Tracking[C],2022.
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