  Relying on Transformer for complex visual feature learning, object tracking
has witnessed the new standard for state-of-the-arts (SOTAs). However, this
advancement accompanies by larger training data and longer training period,
making tracking increasingly expensive. In this paper, we demonstrate that the
Transformer-reliance is not necessary and the pure ConvNets are still
competitive and even better yet more economical and friendly in achieving SOTA
tracking. Our solution is to unleash the power of multimodal vision-language
(VL) tracking, simply using ConvNets. The essence lies in learning novel
unified-adaptive VL representations with our modality mixer (ModaMixer) and
asymmetrical ConvNet search. We show that our unified-adaptive VL
representation, learned purely with the ConvNets, is a simple yet strong
alternative to Transformer visual features, by unbelievably improving a
CNN-based Siamese tracker by 14.5% in SUC on challenging LaSOT (50.7% > 65.2%),
even outperforming several Transformer-based SOTA trackers. Besides empirical
results, we theoretically analyze our approach to evidence its effectiveness.
By revealing the potential of VL representation, we expect the community to
divert more attention to VL tracking and hope to open more possibilities for
future tracking beyond Transformer. Code and models will be released at
https://github.com/JudasDie/SOTS.
