# Predictively Encoded Graph Convolutional Network for Noise-Robust Skeleton-based Action Recognition

arXiv: https://arxiv.org/abs/2003.07514 · HTML: https://mlsanity.com/paper/2003.07514/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video Surveillance](https://mlsanity.com/paper/2201.05739/) (2022)
- [3D Skeleton-Based Action Recognition: A Review](https://mlsanity.com/paper/2506.00915/) (2025)
- [Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN)](https://mlsanity.com/paper/2301.13360/) (2023)
- [Action Recognition with Domain Invariant Features of Skeleton Image](https://mlsanity.com/paper/2111.11250/) (2021)
- [Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation](https://mlsanity.com/paper/2404.18206/) (2024)
- [Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)](https://mlsanity.com/paper/2411.06553/) (2024)
- [Action Recognition in Real-World Ambient Assisted Living Environment](https://mlsanity.com/paper/2503.23214/) (2025)
- [A New Adjacency Matrix Configuration in GCN-based Models for Skeleton-based Action Recognition](https://mlsanity.com/paper/2206.14344/) (2022)

## Abstract (arXiv)

In skeleton-based action recognition, graph convolutional networks (GCNs), which model human body skeletons using graphical components such as nodes and connections, have achieved remarkable performance recently. However, current state-of-the-art methods for skeleton-based action recognition usually work on the assumption that the completely observed skeletons will be provided. This may be problematic to apply this assumption in real scenarios since there is always a possibility that captured skeletons are incomplete or noisy. In this work, we propose a skeleton-based action recognition method which is robust to noise information of given skeleton features. The key insight of our approach is to train a model by maximizing the mutual information between normal and noisy skeletons using a predictive coding manner. We have conducted comprehensive experiments about skeleton-based action recognition with defected skeletons using NTU-RGB+D and Kinetics-Skeleton datasets. The experimental results demonstrate that our approach achieves outstanding performance when skeleton samples are noised compared with existing state-of-the-art methods.