# Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns

arXiv: https://arxiv.org/abs/2205.14405 · HTML: https://mlsanity.com/paper/2205.14405/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 28 May 2022.

## Builds on (most cited first, 22 of 22)

- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [Learning Trajectory Dependencies for Human Motion Prediction](https://mlsanity.com/paper/1908.05436/) (2019)
- [Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/1904.12659/) (2019)
- [An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1902.09130/) (2019)
- [Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition](https://mlsanity.com/paper/1607.07043/) (2016)
- [Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition](https://mlsanity.com/paper/1904.01189/) (2019)
- [An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data](https://mlsanity.com/paper/1611.06067/) (2016)
- [A New Representation of Skeleton Sequences for 3D Action Recognition](https://mlsanity.com/paper/1703.03492/) (2017)
- [Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation](https://mlsanity.com/paper/1804.06055/) (2018)
- [View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data](https://mlsanity.com/paper/1703.08274/) (2017)
- [SkeleMotion: A New Representation of Skeleton Joint Sequences Based on Motion Information for 3D Action Recognition](https://mlsanity.com/paper/1907.13025/) (2019)
- [Memory Attention Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/1804.08254/) (2018)
- [Rank Pooling for Action Recognition](https://mlsanity.com/paper/1512.01848/) (2015)
- [A Comparative Review of Recent Kinect-based Action Recognition Algorithms](https://mlsanity.com/paper/1906.09955/) (2019)
- [Skeleton-Based Online Action Prediction Using Scale Selection Network](https://mlsanity.com/paper/1902.03084/) (2019)
- [Non-Linear Temporal Subspace Representations for Activity Recognition](https://mlsanity.com/paper/1803.11064/) (2018)
- [Generalized Rank Pooling for Activity Recognition](https://mlsanity.com/paper/1704.02112/) (2017)
- [Discriminatively Learned Hierarchical Rank Pooling Networks](https://mlsanity.com/paper/1705.10420/) (2017)

## Built on by (most cited first, 3 of 3)

- [Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed Transformer](https://mlsanity.com/paper/2407.12322/) (2024)
- [Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment](https://mlsanity.com/paper/2402.04599/) (2024)
- [SRENet: Spectral Re-Entry Network for Point Cloud Action Recognition](https://mlsanity.com/paper/2606.03160/) (2026)

## Abstract (arXiv)

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.