AR-Net: Adaptive Frame Resolution for Efficient Action Recognition

Understand

Action recognition is an open and challenging problem in computer vision.

• While current state-of-the-art models offer excellent recognition results, their computational expense limits their impact for many real-world applications.
• In this paper, we propose a novel approach, called AR-Net (Adaptive Resolution Network), that selects on-the-fly the optimal resolution for each frame conditioned on the input for efficient action recognition in long untrimmed videos.
• Specifically, given a video frame, a policy network is used to decide what input resolution should be used for processing by the action recognition model, with the goal of improving both accuracy and efficiency.