  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.
