论文标题
ASL识别基于公制的轻量级网络
ASL Recognition with Metric-Learning based Lightweight Network
论文作者
论文摘要
在过去的几十年中，机器解决的一系列人类任务急剧扩展。从简单的图像分类问题开始，研究人员现在朝着解决更复杂和重要的问题，例如自主驾驶和语言翻译。语言翻译的情况包括一个具有挑战性的手语翻译领域，该领域同时包含图像和语言处理。我们通过提出一个轻巧的网络来朝着这个方向迈出一步，用于ASL手势识别，具有足以实用应用的性能。提出的解决方案在MS-ASL数据集和实时模式下显示了令人印象深刻的鲁棒性，以进行连续的标志手势识别方案。此外，我们描述了如何将动作识别模型培训与度量学习结合在一起，以在有限大小的数据库上训练网络。培训代码可作为英特尔OpenVino培训扩展的一部分获得。
In the past decades the set of human tasks that are solved by machines was extended dramatically. From simple image classification problems researchers now move towards solving more sophisticated and vital problems, like, autonomous driving and language translation. The case of language translation includes a challenging area of sign language translation that incorporates both image and language processing. We make a step in that direction by proposing a lightweight network for ASL gesture recognition with a performance sufficient for practical applications. The proposed solution demonstrates impressive robustness on MS-ASL dataset and in live mode for continuous sign gesture recognition scenario. Additionally, we describe how to combine action recognition model training with metric-learning to train the network on the database of limited size. The training code is available as part of Intel OpenVINO Training Extensions.
