  High-performance Transformer trackers have shown excellent results, yet they
often bear a heavy computational load. Observing that a smaller input can
immediately and conveniently reduce computations without changing the model, an
easy solution is to adopt the low-resolution input for efficient Transformer
tracking. Albeit faster, this hurts tracking accuracy much due to information
loss in low resolution tracking. In this paper, we aim to mitigate such
information loss to boost the performance of the low-resolution Transformer
tracking via dual knowledge distillation from a frozen high-resolution (but not
a larger) Transformer tracker. The core lies in two simple yet effective
distillation modules, comprising query-key-value knowledge distillation
(QKV-KD) and discrimination knowledge distillation (Disc-KD), across
resolutions. The former, from the global view, allows the low-resolution
tracker to inherit the features and interactions from the high-resolution
tracker, while the later, from the target-aware view, enhances the
target-background distinguishing capacity via imitating discriminative regions
from its high-resolution counterpart. With the dual knowledge distillation, our
Low-Resolution Transformer Tracker (LoReTrack) enjoys not only high efficiency
owing to reduced computation but also enhanced accuracy by distilling knowledge
from the high-resolution tracker. In extensive experiments, LoReTrack with a
256x256 resolution consistently improves baseline with the same resolution, and
shows competitive or even better results compared to 384x384 high-resolution
Transformer tracker, while running 52% faster and saving 56% MACs. Moreover,
LoReTrack is resolution-scalable. With a 128x128 resolution, it runs 25 fps on
a CPU with 64.9%/46.4% SUC scores on LaSOT/LaSOText, surpassing all other CPU
real-time trackers. Code will be released.
