Looking Fast and Slow: Memory-Guided Mobile Video Object Detection

TL;DR

This paper introduces a memory-guided approach for mobile video object detection that mimics human scene understanding, achieving high accuracy and speed by combining lightweight and conventional feature extractors with adaptive inference policies.

Contribution

It proposes a novel memory-based detection framework that reduces computation and improves accuracy on mobile devices, utilizing reinforcement learning for adaptive inference.

Findings

Achieves state-of-the-art accuracy among mobile methods on Imagenet VID 2015

Runs at over 70 FPS on a Pixel 3 phone

Uses minimal computation with scene gist recognition for efficient detection

Abstract

With a single eye fixation lasting a fraction of a second, the human visual system is capable of forming a rich representation of a complex environment, reaching a holistic understanding which facilitates object recognition and detection. This phenomenon is known as recognizing the "gist" of the scene and is accomplished by relying on relevant prior knowledge. This paper addresses the analogous question of whether using memory in computer vision systems can not only improve the accuracy of object detection in video streams, but also reduce the computation time. By interleaving conventional feature extractors with extremely lightweight ones which only need to recognize the gist of the scene, we show that minimal computation is required to produce accurate detections when temporal memory is present. In addition, we show that the memory contains enough information for deploying reinforcement learning algorithms to learn an adaptive inference policy. Our model achieves state-of-the-art performance among mobile methods on the Imagenet VID 2015 dataset, while running at speeds of up to 70+ FPS on a Pixel 3 phone.