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

In particular, the non-linear model is often optimized, whereas the reference KF model is not. We argue that both should be optimized similarly, and to that end present the Optimized KF ( OKF).

Both challenges involve modeling long-term dependencies.

Over time, the two branches interact recurrently to build a scene representation from moving fixations.

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

In a standard view of the reinforcement learning problem, an agent's goal is to efficiently identify a policy that maximizes long-term reward.