# Part-based Graph Convolutional Network for Action Recognition

arXiv: https://arxiv.org/abs/1809.04983 · HTML: https://mlsanity.com/paper/1809.04983/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2018.

## Built on by (most cited first, 19 of 19)

- [Generative Action Description Prompts for Skeleton-based Action Recognition](https://mlsanity.com/paper/2208.05318/) (2022)
- [STAR-Transformer: A Spatio-temporal Cross Attention Transformer for Human Action Recognition](https://mlsanity.com/paper/2210.07503/) (2022)
- [Hypergraph Transformer for Skeleton-based Action Recognition](https://mlsanity.com/paper/2211.09590/) (2022)
- [IIP-Transformer: Intra-Inter-Part Transformer for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2110.13385/) (2021)
- [PSUMNet: Unified Modality Part Streams are All You Need for Efficient Pose-based Action Recognition](https://mlsanity.com/paper/2208.05775/) (2022)
- [Graph-based Normalizing Flow for Human Motion Generation and Reconstruction](https://mlsanity.com/paper/2104.03020/) (2021)
- [Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2112.10570/) (2021)
- [Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition](https://mlsanity.com/paper/2209.10073/) (2022)
- [A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities](https://mlsanity.com/paper/2408.05436/) (2024)
- [Spatial Temporal Graph Attention Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2208.08599/) (2022)
- [Deep video representation learning: a survey](https://mlsanity.com/paper/2405.06574/) (2024)
- [BabyNet: A Lightweight Network for Infant Reaching Action Recognition in Unconstrained Environments to Support Future Pediatric Rehabilitation Applications](https://mlsanity.com/paper/2208.04950/) (2022)
- [A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities](https://mlsanity.com/paper/2409.09678/) (2024)
- [Pose-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2210.06192/) (2022)
- [Multi-Graph Convolution Network for Pose Forecasting](https://mlsanity.com/paper/2304.04956/) (2023)
- [Is end-to-end learning enough for fitness activity recognition?](https://mlsanity.com/paper/2305.08191/) (2023)
- [Multi-encoder nnU-Net outperforms transformer models with self-supervised pretraining](https://mlsanity.com/paper/2504.03474/) (2025)
- [HFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2501.11007/) (2025)
- [Partial Skeleton Visibility for Action Recognition: A Constrained Field-of-View Approach](https://mlsanity.com/paper/2607.00716/) (2026)

## Abstract (arXiv)

Human actions comprise of joint motion of articulated body parts or `gestures'. Human skeleton is intuitively represented as a sparse graph with joints as nodes and natural connections between them as edges. Graph convolutional networks have been used to recognize actions from skeletal videos. We introduce a part-based graph convolutional network (PB-GCN) for this task, inspired by Deformable Part-based Models (DPMs). We divide the skeleton graph into four subgraphs with joints shared across them and learn a recognition model using a part-based graph convolutional network. We show that such a model improves performance of recognition, compared to a model using entire skeleton graph. Instead of using 3D joint coordinates as node features, we show that using relative coordinates and temporal displacements boosts performance. Our model achieves state-of-the-art performance on two challenging benchmark datasets NTURGB+D and HDM05, for skeletal action recognition.