{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/prompting-for-multi-modal-tracking","title":"Prompting for Multi-Modal Tracking","arxiv_id":"2207.14571","date":"2022-07-29","proceeding":null,"authors":["Jinyu Yang","Zhe Li","Feng Zheng","Aleš Leonardis","Jingkuan Song"],"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.","url_abs":"https://arxiv.org/abs/2207.14571v2","url_pdf":"https://arxiv.org/pdf/2207.14571v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"rgb-t-tracking","task_name":"Rgb-T Tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/rgb-t-tracking-on-lasher","task":"Rgb-T Tracking","dataset":"LasHeR","model":"ProTrack","rank_in_archive_order":34,"of":39,"metrics":{"Precision":"50.9","Success":"42.1"},"uses_additional_data":false},{"leaderboard":"/sota/rgb-t-tracking-on-rgbt234","task":"Rgb-T Tracking","dataset":"RGBT234","model":"ProTrack","rank_in_archive_order":40,"of":42,"metrics":{"Precision":"78.6","Success":"58.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.14571","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}