This paper introduces AutoGCN, a generic Neural Architecture Search (NAS) algorithm for Human Activity Recognition (HAR) using Graph Convolution Networks (GCNs). HAR has gained attention due to advances in deep learning, increased data availability, and enhanced computational capabilities. At the same time, GCNs have shown promising results in modeling relationships between body key points in a skeletal graph. While domain experts often craft dataset-specific GCN-based methods, their applicability beyond this specific context is severely limited. AutoGCN seeks to address this limitation by simultaneously searching for the ideal hyperparameters and architecture combination within a versatile search space using a reinforcement controller while balancing optimal exploration and exploitation behavior with a knowledge reservoir during the search process. We conduct extensive experiments on two large-scale datasets focused on skeleton-based action recognition to assess the proposed algorithm's performance. Our experimental results underscore the effectiveness of AutoGCN in constructing optimal GCN architectures for HAR, outperforming conventional NAS and GCN methods, as well as random search. These findings highlight the significance of a diverse search space and an expressive input representation to enhance the network performance and generalizability.

翻译：本文提出AutoGCN，一种用于人体行为识别（HAR）的通用神经架构搜索（NAS）算法，该算法基于图卷积网络（GCN）。得益于深度学习的进展、数据可用性的提升以及计算能力的增强，HAR领域已获得广泛关注。与此同时，GCN在建模骨骼图中人体关键点之间的关系方面展现出显著成效。尽管领域专家常针对特定数据集设计基于GCN的方法，但这些方法在该特定场景之外的适用性极为有限。AutoGCN旨在通过以下方式解决这一局限：在灵活搜索空间中，利用强化控制器同步搜索最优超参数与架构组合，并在搜索过程中借助知识库平衡最优探索与利用行为。我们在两个专注于骨骼动作识别的大规模数据集上开展广泛实验，评估所提算法的性能。实验结果表明，AutoGCN在构建面向HAR的最优GCN架构方面具有显著有效性，其性能优于传统NAS方法、GCN方法及随机搜索。这些发现强调了多样化搜索空间及富有表达力的输入表示对于增强网络性能与泛化能力的重要性。