Tracking by natural language specification (TNL) aims to consistently
localize a target in a video sequence given a linguistic description in the
initial frame. Existing methodologies perform language-based and template-based
matching for target reasoning separately and merge the matching results from
two sources, which suffer from tracking drift when language and visual
templates miss-align with the dynamic target state and ambiguity in the later
merging stage. To tackle the issues, we propose a joint multi-modal tracking
framework with 1) a prompt modulation module to leverage the complementarity
between temporal visual templates and language expressions, enabling precise
and context-aware appearance and linguistic cues, and 2) a unified target
decoding module to integrate the multi-modal reference cues and executes the
integrated queries on the search image to predict the target location in an
end-to-end manner directly. This design ensures spatio-temporal consistency by
leveraging historical visual information and introduces an integrated solution,
generating predictions in a single step. Extensive experiments conducted on
TNL2K, OTB-Lang, LaSOT, and RefCOCOg validate the efficacy of our proposed
approach. The results demonstrate competitive performance against
state-of-the-art methods for both tracking and grounding.

通过自然语言规范（TNL）追踪是在视频序列中根据初始帧的语言描述来稳定地定位目标的方法。本文提出了一种联合多模态追踪框架，通过提示调制模块和统一目标解码模块实现了时间视觉模板和语言表达之间的补充性，并直接在搜索图像上执行整合的查询，以一步预测目标位置。对 TNL2K、OTB-Lang、LaSOT 和 RefCOCOg 进行了大量实验证明了我们所提出方法的有效性，在追踪和地面连接方面与现有方法具有竞争性能。