# Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition

arXiv: https://arxiv.org/abs/2007.03263 · HTML: https://mlsanity.com/paper/2007.03263/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 21 of 21)

- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [Generative Action Description Prompts for Skeleton-based Action Recognition](https://mlsanity.com/paper/2208.05318/) (2022)
- [Hypergraph Transformer for Skeleton-based Action Recognition](https://mlsanity.com/paper/2211.09590/) (2022)
- [Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations](https://mlsanity.com/paper/2211.13466/) (2022)
- [DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition](https://mlsanity.com/paper/2210.05895/) (2022)
- [Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning](https://mlsanity.com/paper/2308.03975/) (2023)
- [Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2201.02849/) (2022)
- [STEP CATFormer: Spatial-Temporal Effective Body-Part Cross Attention Transformer for Skeleton-based Action Recognition](https://mlsanity.com/paper/2312.03288/) (2023)
- [Skeleton-based Action Recognition through Contrasting Two-Stream Spatial-Temporal Networks](https://mlsanity.com/paper/2301.11495/) (2023)
- [The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition](https://mlsanity.com/paper/2105.13061/) (2021)
- [Overcoming Topology Agnosticism: Enhancing Skeleton-Based Action Recognition through Redefined Skeletal Topology Awareness](https://mlsanity.com/paper/2305.11468/) (2023)
- [Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video Surveillance](https://mlsanity.com/paper/2201.05739/) (2022)
- [Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond](https://mlsanity.com/paper/2406.02978/) (2024)
- [Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition](https://mlsanity.com/paper/2407.12312/) (2024)
- [Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2202.02314/) (2022)
- [Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN](https://mlsanity.com/paper/2406.15003/) (2024)
- [Self-attention based anchor proposal for skeleton-based action recognition](https://mlsanity.com/paper/2112.09413/) (2021)
- [Pose-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2210.06192/) (2022)
- [Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2302.12967/) (2023)
- [SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition](https://mlsanity.com/paper/2308.16018/) (2023)
- [Severe Domain Shift in Skeleton-Based Action Recognition:A Study of Uncertainty Failure in Real-World Gym Environments](https://mlsanity.com/paper/2603.15574/) (2026)

## Abstract (arXiv)

Dynamic skeletal data, represented as the 2D/3D coordinates of human joints, has been widely studied for human action recognition due to its high-level semantic information and environmental robustness. However, previous methods heavily rely on designing hand-crafted traversal rules or graph topologies to draw dependencies between the joints, which are limited in performance and generalizability. In this work, we present a novel decoupled spatial-temporal attention network(DSTA-Net) for skeleton-based action recognition. It involves solely the attention blocks, allowing for modeling spatial-temporal dependencies between joints without the requirement of knowing their positions or mutual connections. Specifically, to meet the specific requirements of the skeletal data, three techniques are proposed for building attention blocks, namely, spatial-temporal attention decoupling, decoupled position encoding and spatial global regularization. Besides, from the data aspect, we introduce a skeletal data decoupling technique to emphasize the specific characteristics of space/time and different motion scales, resulting in a more comprehensive understanding of the human actions.To test the effectiveness of the proposed method, extensive experiments are conducted on four challenging datasets for skeleton-based gesture and action recognition, namely, SHREC, DHG, NTU-60 and NTU-120, where DSTA-Net achieves state-of-the-art performance on all of them.