Object tracking is a fundamental task in computer vision, requiring the localization of objects of interest across video frames.

To achieve real-time response, recent studies have focused on lightweight neural network models.

Further analysis substantiates our hypothesis that our improvement can be attributed to reduced overfitting to instruction tuning datasets. It is worth noting that we are not proposing IM as a replacement for the current instruction tuning process. Instead, our work aims to provide practical guidance for instruction tuning LMs, especially in low-resource scenarios.

Forward-mode AD that are closer estimations of the true gradients.