论文标题
动态手势识别的知识共享模型的合奏
An Ensemble of Knowledge Sharing Models for Dynamic Hand Gesture Recognition
论文作者
论文摘要
本文的重点是在人与机器之间相互作用的背景下动态的手势识别。我们提出了一个由两个子网组成的模型，一个基于变压器和一个有序的神经元长期内存（ON-LSTM）的复发性神经网络（RNN）。每个子网络都经过训练，只使用骨骼关节执行手势识别的任务。由于每个子网络由于体系结构的差异而提取不同类型的功能，因此可以在子网络之间共享知识。通过知识蒸馏，每个子网络中的特征和预测都融合为新的融合分类器。此外，可以使用周期性学习率来生成一系列合并组合的模型，以产生更概括的预测。所提出的知识共享模型的合奏仅使用骨骼信息显示了86.11％的总体精度，如使用动态手势-14/28数据集测试
The focus of this paper is dynamic gesture recognition in the context of the interaction between humans and machines. We propose a model consisting of two sub-networks, a transformer and an ordered-neuron long-short-term-memory (ON-LSTM) based recurrent neural network (RNN). Each sub-network is trained to perform the task of gesture recognition using only skeleton joints. Since each sub-network extracts different types of features due to the difference in architecture, the knowledge can be shared between the sub-networks. Through knowledge distillation, the features and predictions from each sub-network are fused together into a new fusion classifier. In addition, a cyclical learning rate can be used to generate a series of models that are combined in an ensemble, in order to yield a more generalizable prediction. The proposed ensemble of knowledge-sharing models exhibits an overall accuracy of 86.11% using only skeleton information, as tested using the Dynamic Hand Gesture-14/28 dataset
