VA-RED$^2$: Video Adaptive Redundancy Reduction

Understand

Performing inference on deep learning models for videos remains a challenge due to the large amount of computational resources required to achieve robust recognition.

• An inherent property of real-world videos is the high correlation of information across frames which can translate into redundancy in either temporal or spatial feature maps of the models, or both.
• The type of redundant features depends on the dynamics and type of events in the video: static videos have more temporal redundancy while videos focusing on objects tend to have more channel redundancy.
• Here we present a redundancy reduction framework, termed VA-RED$^2$, which is input-dependent.