论文标题
通过利用时间模式来增强骨骼动作识别器
Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns
论文作者
论文摘要
骨架序列是紧凑而轻巧的。已经提出了许多基于骨架的动作识别者来对人类行为进行分类。在这项工作中，我们旨在结合与现有模型兼容的组件，并进一步提高其准确性。为此，我们设计了两个时间配件：离散的余弦编码（DCE）和按时间顺序损失（CRL）。 DCE促进模型以分析频域的运动模式，同时减轻信号噪声的影响。 CRL指导网络明确捕获序列的时间顺序。这两个组件始终将许多最近提供的动作识别器具有准确的提升，从而在两个大型数据集上实现了新的最先进（SOTA）精度。
Skeleton sequences are compact and lightweight. Numerous skeleton-based action recognizers have been proposed to classify human behaviors. In this work, we aim to incorporate components that are compatible with existing models and further improve their accuracy. To this end, we design two temporal accessories: discrete cosine encoding (DCE) and chronological loss (CRL). DCE facilitates models to analyze motion patterns from the frequency domain and meanwhile alleviates the influence of signal noise. CRL guides networks to explicitly capture the sequence's chronological order. These two components consistently endow many recently-proposed action recognizers with accuracy boosts, achieving new state-of-the-art (SOTA) accuracy on two large datasets.
