超越显式语言：面向通用目标跟踪的即插即用视觉到语言建模
Beyond Explicit Language: Plug-and-Play Visual-to-Linguistic Modeling Toward General Object Tracking
提出一个即插即用的文本反转模块，将模板和搜索区域的视觉特征映射到CLIP文本嵌入空间，无需显式语言输入即可为跟踪器注入语言引导，可复用于其他视觉任务以隐式利用语言先验。
原文依据 The proposed textual inversion module converts visual features from template and search regions into text tokens in the CLIP text embedding space.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论自然语言为增强视觉目标跟踪提供了有价值的辅助信息。现有视觉语言跟踪方法显式利用语言描述来辅助跟踪，但存在两个关键局限：无法根据运动目标和变化上下文动态调整描述；对语言输入的强依赖可能导致文本不可用时跟踪失败。为解决这些问题，我们设计了一个简单有效的即插即用模块，隐式利用语言辅助而无需显式语言输入。所提出的文本反转模块将模板和搜索区域的视觉特征转换为CLIP文本嵌入空间中的文本标记。它有效地将视觉表示反转为语言形式，整合来自模板和搜索区域的上下文信息。随后，语言线索通过多层语义注入机制注入视觉特征空间。该设计增强了跨模态特征表示的完整性和模态间语义对齐的准确性，从而为通用目标跟踪实现动态更新的语言信息引导。大量实验证明了所提方法的有效性。我们将所提模块集成到多个先进跟踪器中，并在视觉和视觉语言跟踪数据集上评估，包括MCITrack、DUTrack和SeqTrack。仅训练新引入的模块和相应解码器，所提方法以最小计算开销实现了显著性能提升。代码将公开。
英文原文摘要 1689 字符
Natural language provides valuable auxiliary information for enhancing visual object tracking. While existing vision-language tracking methods explicitly leverage linguistic descriptions to aid tracking, they suffer from two critical limitations: the inability to dynamically adapt descriptions to the moving target and changing context; and the strong dependency on language input may causes failure when text is unavailable. To address the issues, we design a simple yet effective plug-and-play module that leverages linguistic assistance implicitly, without requiring explicit language input. The proposed textual inversion module converts visual features from template and search regions into text tokens in the CLIP text embedding space. It effectively inverts visual representations into linguistic forms, integrating contextual information from the both template and search region. The linguistic cues are then injected into the visual feature space via a multi-layer semantic injection mechanism. The design enhances the completeness of cross-modal feature representations and the accuracy of inter-modal semantic alignment, thus enabling dynamically updated linguistic information guidance for general object tracking. Extensive experiments demonstrate the effectiveness of our proposed method. We integrate the proposed module into several advanced trackers and evaluate on both visual and vision-language tracking datasets, including MCITrack, DUTrack, and SeqTrack. By training only the newly introduced module and the corresponding decoder, the proposed approach achieves significant performance gains with minimal computational overhead. Code will be made publicly available.