Recent years have witnessed great progress in deep neural networks for
real-time applications. However, most existing works do not explicitly consider
the general case where the device's state and the available resources fluctuate
over time, and none of them investigate or address the impact of varying
computational resources for online video understanding tasks. This paper
proposes a System-status-aware Adaptive Network (SAN) that considers the
device's real-time state to provide high-quality predictions with low delay.
Usage of our agent's policy improves efficiency and robustness to fluctuations
of the system status. On two widely used video understanding tasks, SAN obtains
state-of-the-art performance while constantly keeping processing delays low.
Moreover, training such an agent on various types of hardware configurations is
not easy as the labeled training data might not be available, or can be
computationally prohibitive. To address this challenging problem, we propose a
Meta Self-supervised Adaptation (MSA) method that adapts the agent's policy to
new hardware configurations at test-time, allowing for easy deployment of the
model onto other unseen hardware platforms.

本文提出了一种系统状态感知的自适应网络，对于在线视频理解任务，该网络可以根据实时设备状态提供高质量的预测并降低延迟，同时通过元自监督适应方法来适应新的硬件配置，实现模型在其他未见过的硬件平台上的部署。