In vision-enabled autonomous systems such as robots and autonomous cars,
video object detection plays a crucial role, and both its speed and accuracy
are important factors to provide reliable operation. The key insight we show in
this paper is that speed and accuracy are not necessarily a trade-off when it
comes to image scaling. Our results show that re-scaling the image to a lower
resolution will sometimes produce better accuracy. Based on this observation,
we propose a novel approach, dubbed AdaScale, which adaptively selects the
input image scale that improves both accuracy and speed for video object
detection. To this end, our results on ImageNet VID and mini
YouTube-BoundingBoxes datasets demonstrate 1.3 points and 2.7 points mAP
improvement with 1.6x and 1.8x speedup, respectively. Additionally, we improve
state-of-the-art video acceleration work by an extra 1.25x speedup with
slightly better mAP on ImageNet VID dataset.

本文介绍了一种被称为 AdaScale 的新方法，该方法在视频对象检测方面提供了更好的准确性和速度，并展示了 - imagenet VID 和 mini YouTube - 边界框数据集上的结果。