LoopTrack: A Simple Baseline for Parameter-Efficient Transformer Tracking

Current Transformer-based tracking methods typically stack multiple Transformer blocks with separate parameters to model interactions between the target template and the search region for target localization. Despite excellent performance, these trackers often incur substantial parameter overhead from stacked blocks, making their deployment on resource-limited devices difficult. To address this, we propose a novel parameter-efficient Transformer tracking framework, dubbed ***LoopTrack***, which repeatedly applies a small set of Transformer blocks with shared parameters to interact features in a looped architecture for tracking, significantly reducing the number of parameters. To further exploit target cues for improving LoopTrack, we present two lightweight designs, including *target-aware looping* (TAL) and *gated target memory* (GTM). The former applies intermediate target information generated by one loop to guide feature interaction in the subsequent loop, enabling progressive feature refinement, while the latter maintains a compact memory across frames, which is incorporated into the loop process to provide long-term information to the tracker, mitigating temporal drift in tracking. Compared to existing Transformer trackers, LoopTrack enables multiple rounds of feature interaction with fewer model parameters, making it resource-friendly for deployment. In extensive experiments on multiple datasets, LoopTrack shows promising results with a favorable accuracy-parameter trade-off. In particular, our $\text{LoopTrack}\_\text{One}$ , with a single shared Transformer block, achieves 66.2% SUC score on LaSOT with only 3.4M parameters, while $\text{LoopTrack}\_\text{Three}$ , using three shared blocks, achieves 69.3% SUC score with 6.4M model parameters, surpassing existing parameter-efficient tracking methods with comparable or larger model size. With LoopTrack, we aim to establish a simple yet strong baseline for parameter-efficient Transformer tracking. Our code and models will be released.