Overcoming Feature Contamination by Unidirectional Information Modeling for Vision-Language Tracking

Benefiting from the advantages of multi-modal learning, Vision-Language Tracking shows greater potential than Visual Tracking. Existing work utilizes one-stream structures to fuse vision and language features, resulting in noise propagation from the search region into the language features. This contamination weakens the guidance of language information, consequently limiting the robustness of the tracking model. To solve this problem, we propose a Unidirectional Information modeling (UITracker) to explicitly fuse the language and visual features for Vision-Language Tracking. Specifically, we introduce a plug-and-play lightweight modal adapter to unidirectionally inject language guidance into the visual template and search region across all layers. This allows the tracker to make full use of rich semantic information while overcoming language feature contamination in the feature interaction process. Extensive ablation studies demonstrate the superiority and effectiveness of our UITracker. Code and raw results are available at https://github.com/jcwang0602/UITrack.