Abstract
One-stream Transformer-based trackers achieve advanced performance in visual object tracking but suffer from significant computational overhead that hinders real-time deployment. While token pruning offers a path to efficiency, existing methods are fragmented. They typically prune the search region, dynamic template, and static template in isolation, overlooking critical inter-component dependencies, which yields suboptimal pruning and degraded accuracy. To address this, we introduce UTPTrack, a simple and Unified Token Pruning framework that, for the first time, jointly compresses all three components. UTPTrack employs an attention-guided, token type-aware strategy to holistically model redundancy, a design that seamlessly supports unified tracking across multimodal and language-guided tasks within a single model. Extensive evaluations on 10 benchmarks demonstrate that UTPTrack achieves a new state-of-the-art in the accuracy-efficiency trade-off for pruning-based trackers, pruning 65.4% of vision tokens in RGB-based tracking and 67.5% in unified tracking while preserving 99.7% and 100.5% of baseline performance, respectively. This strong performance across both RGB and multimodal scenarios underlines its potential as a robust foundation for future research in efficient visual tracking. Code will be released at this https URL.
Abstract (translated)
基于单流Transformer的跟踪器在视觉对象跟踪中表现出色，但计算开销大，阻碍了其实时部署。虽然令牌剪枝提供了一种提高效率的方法，但现有方法是孤立地对搜索区域、动态模板和静态模板进行剪枝，忽视了各组件间的相互依赖关系，导致次优的剪枝效果及准确率下降。为解决此问题，我们提出了UTPTrack——一个简单且统一化的令牌剪枝框架，首次实现了同时压缩所有三个组成部分的目标。UTPTrack采用注意力引导、令牌类型感知策略来整体建模冗余，设计上可无缝支持单一模型内跨多模态和语言指导任务的统一跟踪。在10个基准测试上的广泛评估表明，UTPTrack在基于剪枝方法的精度效率权衡中达到了新的最先进水平，在基于RGB的跟踪中剪除了65.4%的视觉令牌，并在统一路线中剪除67.5%，同时分别保留了基线性能的99.7%和100.5%。无论是在RGB还是多模态场景下的强大表现，均突显出UTPTrack作为未来高效视觉跟踪研究稳健基础的巨大潜力。代码将在此网址发布。
URL
https://arxiv.org/abs/2602.23734
