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 recent years, deep neural networks (DNNs) have witnessed extensive applications, and protecting their intellectual property (IP) is thus crucial. As a noninvasive way for model IP protection, model fingerprinting has become popular.

We show here that this approach can effectively be used to make a large pretrained network equivariant.