论文标题
人类动作识别具有大规模脑启发的光子计算机的识别
Human action recognition with a large-scale brain-inspired photonic computer
论文作者
论文摘要
在视频流中对人类行为的认识是计算机视觉中的一项艰巨任务，例如，主要的应用程序应用程序。大脑计算机界面和监视。深度学习最近显示出了出色的结果，但是在实践中很难使用，因为其培训需要大型数据集和特殊用途，即消耗能量的硬件。在这项工作中，我们提出了一种基于储层计算范式的可扩展光子神经启发的体系结构，该体系结构能够以最先进的精度识别基于视频的人类动作。我们的实验光学设置包括现成的组件，并实现了易于训练的大型平行复发网络，并且可以扩展到数十万个节点。这项工作为简单地重新配置和节能的光子信息处理系统铺平了道路，用于实时视频处理。
The recognition of human actions in video streams is a challenging task in computer vision, with cardinal applications in e.g. brain-computer interface and surveillance. Deep learning has shown remarkable results recently, but can be found hard to use in practice, as its training requires large datasets and special purpose, energy-consuming hardware. In this work, we propose a scalable photonic neuro-inspired architecture based on the reservoir computing paradigm, capable of recognising video-based human actions with state-of-the-art accuracy. Our experimental optical setup comprises off-the-shelf components, and implements a large parallel recurrent neural network that is easy to train and can be scaled up to hundreds of thousands of nodes. This work paves the way towards simply reconfigurable and energy-efficient photonic information processing systems for real-time video processing.
