Exploring Decoupled Spatio-Temporal Consistency Learning and Self-Prompting Evolution for Self-Supervised Tracking

The success of visual tracking has been largely driven by datasets with manual box annotations. However, these box annotations require tremendous human effort, limiting the scale and diversity of existing tracking datasets. In this work, we present a novel high-performance Self-Supervised Tracking model named SSTrack++, designed to eliminate the need of box annotations. Specifically, we design an effective weak-to-strong self-supervised training framework that aims to narrow the feature distribution gap between labeled and unlabeled frames. Building upon this framework, a decoupled spatio-temporal consistency training strategy is introduced to capture rich target information across timestamps by leveraging global spatial localization and local temporal association. Then, a self-prompting evolution module is designed to jointly mine the target appearance evolution at both the feature and decision levels, enabling more robust adaptation to complex and diverse unlabeled tracking scenarios. Furthermore, an instance contrastive loss is formulated to learn instance-level correspondences from a multi-view perspective, providing robust instance-level supervision without any additional annotations. This new design paradigm enables SSTrack++ to not only learn a generalizable tracking representation in a low-annotation self-supervised manner, but also makes it possible to simulate realistic appearance and motion variations of target instances in real-world scenarios. Extensive experiments on ten benchmark datasets demonstrate that SSTrack++ surpasses SOTA self-supervised tracking methods, achieving an improvement of more than 25.8%, 21.4%, and 15.8% in AUC (AO) score on the GOT10K, LaSOT, TrackingNet datasets, respectively, thereby significantly narrowing the performance gap with fully supervised trackers. Code is available at https://github.com/GXNU-ZhongLab/SSTrack.