  Current state-of-the-art (SOTA) methods in visual object tracking often
require extensive computational resources and vast amounts of training data,
leading to a risk of overfitting. This study introduces a more efficient
training strategy to mitigate overfitting and reduce computational
requirements. We balance the training process with a mix of negative and
positive samples from the outset, named as Joint learning with Negative samples
(JN). Negative samples refer to scenarios where the object from the template is
not present in the search region, which helps to prevent the model from simply
memorizing the target, and instead encourages it to use the template for object
location. To handle the negative samples effectively, we adopt a
distribution-based head, which modeling the bounding box as distribution of
distances to express uncertainty about the target's location in the presence of
negative samples, offering an efficient way to manage the mixed sample
training. Furthermore, our approach introduces a target-indicating token. It
encapsulates the target's precise location within the template image. This
method provides exact boundary details with negligible computational cost but
improving performance. Our model, JN-256, exhibits superior performance on
challenging benchmarks, achieving 75.8% AO on GOT-10k and 84.1% AUC on
TrackingNet. Notably, JN-256 outperforms previous SOTA trackers that utilize
larger models and higher input resolutions, even though it is trained with only
half the number of data sampled used in those works.
