Despite achieving state-of-the-art performance in visual tracking, recent
single-branch trackers tend to overlook the weak prior assumptions associated
with the Vision Transformer (ViT) encoder and inference pipeline. Moreover, the
effectiveness of discriminative trackers remains constrained due to the
adoption of the dual-branch pipeline. To tackle the inferior effectiveness of
the 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 module
and joint target state embedding to enrich the dense embedding paradigm based
on ViT. Then, we combine AViT-Enc with a dense-fusion decoder and a
discriminative target model to predict accurate location. Further, to mitigate
the limitations of conventional inference practice, we present a novel
inference pipeline called CycleTrack, which bolsters the tracking robustness in
the presence of distractors via bidirectional cycle tracking verification.
Lastly, we propose a dual-frame update inference strategy that adeptively
handles significant challenges in long-term scenarios. In the experiments, we
evaluate AViTMP on ten tracking benchmarks for a comprehensive assessment,
including LaSOT, LaSOTExtSub, AVisT, etc. The experimental results
unequivocally establish that AViTMP attains state-of-the-art performance,
especially on long-time tracking and robustness.

提出了一种自适应 ViT 模型预测跟踪器 (AViTMP)，通过引入适配器模块和联合目标状态嵌入来丰富基于 ViT 的稠密嵌入范式，并结合了稠密融合解码器和判别式目标模型以预测准确位置。通过双向循环跟踪验证的循环跟踪方法 CycleTrack 增强了跟踪的鲁棒性，还提出了双帧更新推理策略以有效处理长期场景中的挑战。大量实验证明，AViTMP 在长时间跟踪和鲁棒性方面达到了最先进的性能。