Towards Unified Token Learning for Vision-Language Tracking
Yaozong Zheng Bineng Zhong Qihua Liang Guorong Li Rongrong Ji Xianxian Li
In this paper, we present a simple, flexible and effective vision-language
(VL) tracking pipeline, termed \textbf{MMTrack}, which casts VL tracking as a
token generation task. Traditional paradigms address VL tracking task
indirectly with sophisticated prior designs, making them over-specialize on the
features of specific architectures or mechanisms. In contrast, our proposed
framework serializes language description and bounding box into a sequence of
discrete tokens. In this new design paradigm, all token queries are required to
perceive the desired target and directly predict spatial coordinates of the
target in an auto-regressive manner. The design without other prior modules
avoids multiple sub-tasks learning and hand-designed loss functions,
significantly reducing the complexity of VL tracking modeling and allowing our
tracker to use a simple cross-entropy loss as unified optimization objective
for VL tracking task. Extensive experiments on TNL2K, LaSOT, LaSOT$_{\rm{ext}}$
and OTB99-Lang benchmarks show that our approach achieves promising results,
compared to other state-of-the-arts.