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Despite achieving state-of-the-art performance in visual tracking, recentsingle-branch trackers tend to overlook the weak prior assumptions associatedwith the Vision Transformer (ViT) encoder and inference pipeline. Moreover, theeffectiveness of discriminative trackers remains constrained due to theadoption of the dual-branch pipeline. To tackle the inferior effectiveness ofthe vanilla ViT, we propose an Adaptive ViT Model Prediction tracker (AViTMP)to bridge the gap between single-branch network and discriminative models.Specifically, in the proposed encoder AViT-Enc, we introduce an adaptor moduleand joint target state embedding to enrich the dense embedding paradigm basedon ViT. Then, we combine AViT-Enc with a dense-fusion decoder and adiscriminative target model to predict accurate location. Further, to mitigatethe limitations of conventional inference practice, we present a novelinference pipeline called CycleTrack, which bolsters the tracking robustness inthe presence of distractors via bidirectional cycle tracking verification.Lastly, we propose a dual-frame update inference strategy that adeptivelyhandles significant challenges in long-term scenarios. In the experiments, weevaluate AViTMP on ten tracking benchmarks for a comprehensive assessment,including LaSOT, LaSOTExtSub, AVisT, etc. The experimental resultsunequivocally establish that AViTMP attains state-of-the-art performance,especially on long-time tracking and robustness.

Exploiting Image-Related Inductive Biases in Single-Branch Visual Tracking