GRA:为半监督行动识别进行图形表示对齐
IEEE transactions on neural networks and learning systems
|January 12, 2024
概括
用于动作识别的图形卷积网络 (GCN) 现在可以使用更少的标记数据,这要归功于一种新的自我训练方法. 这种方法还可以提高不完整的骨架数据的性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 图形卷积网络 (GCNs) 是有效的人类行为识别使用骨架数据.
- 目前的GCN方法需要大型标记数据集,这些数据集昂贵且难以获取.
- 在测试过程中,不完整的骨架数据 (缺失的关节/框架) 构成了重大挑战.
研究的目的:
- 开发一种新的方法,即图形表示对齐 (GRA),以解决基于GCN的动作识别中的数据限制.
- 为了减少对广泛标记数据集的依赖,用于训练GCN模型.
- 为了增强GCNs对不完整的骨数据的强度.
主要方法:
- 引入了一种自我训练 (ST) 范式,以生成高质量的伪标签,最大限度地减少对手工标签的需求.
- 实施了使用一致性规范化的表示对齐 (RA) 技术,以减轻缺失数据的影响.
- 对NTU RGB+D和N-UCLA基准进行了GRA方法的评估.
主要成果:
- GRA显著降低了对标记数据的要求,使稳定的模型训练能够在最低限度的监督下进行.
- 表示对齐技术有效地处理不完整的骨架数据,保持高性能.
- 在数据受限制的场景中,GRA 证明了 GCN 性能在数据受限制的场景中得到改善,以及对缺失的数据组件的稳定性.
结论:
- 使用GCNs,GRA提供了一种可行的解决方案,用于数据效率高和可靠的人类行为识别.
- 拟议的方法减轻了与大规模数据采集和数据不完整性相关的实际挑战.
- GRA促进了GCN在现实世界行动识别任务中的应用性.
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