# Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning

arXiv: https://arxiv.org/abs/1805.02335 · HTML: https://mlsanity.com/paper/1805.02335/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2018.

## Built on by (most cited first, 30 of 47)

- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition](https://mlsanity.com/paper/2106.15125/) (2021)
- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [Motion Puzzle: Arbitrary Motion Style Transfer by Body Part](https://mlsanity.com/paper/2202.05274/) (2022)
- [SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos](https://mlsanity.com/paper/2104.11452/) (2021)
- [OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages](https://mlsanity.com/paper/2110.05877/) (2021)
- [LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference](https://mlsanity.com/paper/2309.14331/) (2023)
- [Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition](https://mlsanity.com/paper/2108.04536/) (2021)
- [Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition](https://mlsanity.com/paper/2202.04075/) (2022)
- [AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition](https://mlsanity.com/paper/2103.11770/) (2021)
- [Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions](https://mlsanity.com/paper/2206.12612/) (2022)
- [Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition](https://mlsanity.com/paper/2111.11051/) (2021)
- [GAN-based Reactive Motion Synthesis with Class-aware Discriminators for Human-human Interaction](https://mlsanity.com/paper/2110.00380/) (2021)
- [Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2201.02849/) (2022)
- [Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds](https://mlsanity.com/paper/2207.12659/) (2022)
- [Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble](https://mlsanity.com/paper/2110.06161/) (2021)
- [Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition](https://mlsanity.com/paper/2110.14994/) (2021)
- [Panoramic Human Activity Recognition](https://mlsanity.com/paper/2203.03806/) (2022)
- [Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in Videos](https://mlsanity.com/paper/2404.07645/) (2024)
- [Skeleton-based Action Recognition via Adaptive Cross-Form Learning](https://mlsanity.com/paper/2206.15085/) (2022)
- [CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference](https://mlsanity.com/paper/2209.11904/) (2022)
- [Action Recognition with Multi-stream Motion Modeling and Mutual Information Maximization](https://mlsanity.com/paper/2306.07576/) (2023)
- [Idempotent Unsupervised Representation Learning for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2410.20349/) (2024)
- [HAN: An Efficient Hierarchical Self-Attention Network for Skeleton-Based Gesture Recognition](https://mlsanity.com/paper/2106.13391/) (2021)
- [A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2111.06995/) (2021)
- [Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey](https://mlsanity.com/paper/2310.13039/) (2023)
- [Deep Learning Approaches for Seizure Video Analysis: A Review](https://mlsanity.com/paper/2312.10930/) (2023)
- [MM-WLAuslan: Multi-View Multi-Modal Word-Level Australian Sign Language Recognition Dataset](https://mlsanity.com/paper/2410.19488/) (2024)
- [3D Skeleton-Based Action Recognition: A Review](https://mlsanity.com/paper/2506.00915/) (2025)
- [Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?](https://mlsanity.com/paper/2505.10679/) (2025)

## Abstract (arXiv)

Skeleton-based action recognition has made great progress recently, but many problems still remain unsolved. For example, most of the previous methods model the representations of skeleton sequences without abundant spatial structure information and detailed temporal dynamics features. In this paper, we propose a novel model with spatial reasoning and temporal stack learning (SR-TSL) for skeleton based action recognition, which consists of a spatial reasoning network (SRN) and a temporal stack learning network (TSLN). The SRN can capture the high-level spatial structural information within each frame by a residual graph neural network, while the TSLN can model the detailed temporal dynamics of skeleton sequences by a composition of multiple skip-clip LSTMs. During training, we propose a clip-based incremental loss to optimize the model. We perform extensive experiments on the SYSU 3D Human-Object Interaction dataset and NTU RGB+D dataset and verify the effectiveness of each network of our model. The comparison results illustrate that our approach achieves much better results than state-of-the-art methods.