Dynamic Computational Time for Visual Attention

Understand

We propose a dynamic computational time model to accelerate the average processing time for recurrent visual attention (RAM).

• Rather than attention with a fixed number of steps for each input image, the model learns to decide when to stop on the fly.
• To achieve this, we add an additional continue/stop action per time step to RAM and use reinforcement learning to learn both the optimal attention policy and stopping policy.
• The modification is simple but could dramatically save the average computational time while keeping the same recognition performance as RAM.