mlsanityData to 14 Oct 2026

Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns

arXiv 2205.14405 ↗First posted 28 May 2022cs.CVcited by 3

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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.

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

22 of 45 references.

References · 22

  1. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  2. 2017A Closer Look at Spatiotemporal Convolutions for Action Recognitioncited by 1,143
  3. 2016NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysiscited by 686
  4. 2019NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understandingcited by 533
  5. 2020Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognitioncited by 343
  6. 2019Learning Trajectory Dependencies for Human Motion Predictioncited by 246
  7. 2019Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognitioncited by 228
  8. 2019An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognitioncited by 151
  9. 2016Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognitioncited by 143
  10. 2019Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognitioncited by 135
  11. 2016An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Datacited by 126
  12. 2017A New Representation of Skeleton Sequences for 3D Action Recognitioncited by 126
10 more
  1. 2018Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregationcited by 106
  2. 2017View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Datacited by 105
  3. 2019SkeleMotion: A New Representation of Skeleton Joint Sequences Based on Motion Information for 3D Action Recognitioncited by 43
  4. 2018Memory Attention Networks for Skeleton-based Action Recognitioncited by 36
  5. 2015Rank Pooling for Action Recognitioncited by 29
  6. 2019A Comparative Review of Recent Kinect-based Action Recognition Algorithmscited by 29
  7. 2019Skeleton-Based Online Action Prediction Using Scale Selection Networkcited by 20
  8. 2018Non-Linear Temporal Subspace Representations for Activity Recognitioncited by 11
  9. 2017Generalized Rank Pooling for Activity Recognitioncited by 10
  10. 2017Discriminatively Learned Hierarchical Rank Pooling Networkscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.

  1. 2024Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed Transformercited by 15
  2. 2024Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignmentcited by 10
  3. 2026SRENet: Spectral Re-Entry Network for Point Cloud Action Recognitioncited by 1