Adaptive Target-Oriented Tracking

The current one-stream tracking pipelines are early relation modeling in feature extraction. However, insufficient discrimination may result in ambiguous relation modeling during early feature extraction. Moreover, the non-target information occupies most of the search image, rendering most relation modeling futile. To tackle the above issues, we propose tracking via learning adaptive target-oriented representation, named ATOTrack . We design an Untied positional encoding to mark the template token and the search region token separately, which reduces the confused relationship between the template and the search region. Besides, we introduce an Auto-Mask Learner to decouple the target and non-target information in the search region. Interestingly, the Auto-Mask Learner can self-learn and mask the ineffective information to interpret adaptive target-oriented representation. Extensive experiments demonstrate that ATOTrack is superior to existing methods, which achieves the state-of-the-art performance on six tracking benchmarks. In particular, ATOTrack establishes a new record on AViST with 57% AO. The code and models will be released as soon.