Transformer-based trackers have achieved strong accuracy on the standard
benchmarks. However, their efficiency remains an obstacle to practical
deployment on both GPU and CPU platforms. In this paper, to overcome this
issue, we propose a fully transformer tracking framework, coined as
\emph{MixFormerV2}, without any dense convolutional operation and complex score
prediction module. Our key design is to introduce four special prediction
tokens and concatenate them with the tokens from target template and search
areas. Then, we apply the unified transformer backbone on these mixed token
sequence. These prediction tokens are able to capture the complex correlation
between target template and search area via mixed attentions. Based on them, we
can easily predict the tracking box and estimate its confidence score through
simple MLP heads. To further improve the efficiency of MixFormerV2, we present
a new distillation-based model reduction paradigm, including dense-to-sparse
distillation and deep-to-shallow distillation. The former one aims to transfer
knowledge from the dense-head based MixViT to our fully transformer tracker,
while the latter one is used to prune some layers of the backbone. We
instantiate two types of MixForemrV2, where the MixFormerV2-B achieves an AUC
of 70.6\% on LaSOT and an AUC of 57.4\% on TNL2k with a high GPU speed of 165
FPS, and the MixFormerV2-S surpasses FEAR-L by 2.7\% AUC on LaSOT with a
real-time CPU speed.

本文提出了一种基于全 Transformer 结构的跟踪框架 MixFormerV2，通过引入四个特殊的预测 tokens，结合目标模板和搜索区域的 tokens，采用 Transformer 骨干网络进行跟踪预测，进而通过简单的 MLP heads 预测跟踪框和评估置信度得分，并提出了一种新的蒸馏模型的减少算法，包括密集到稀疏和深层到浅层的蒸馏，以进一步提高跟踪器性能。