# JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition

arXiv: https://arxiv.org/abs/2011.07787 · HTML: https://mlsanity.com/paper/2011.07787/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 30 of 33)

- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [STAR-Transformer: A Spatio-temporal Cross Attention Transformer for Human Action Recognition](https://mlsanity.com/paper/2210.07503/) (2022)
- [Motion Question Answering via Modular Motion Programs](https://mlsanity.com/paper/2305.08953/) (2023)
- [End-to-End Active Speaker Detection](https://mlsanity.com/paper/2203.14250/) (2022)
- [Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling](https://mlsanity.com/paper/2303.15270/) (2023)
- [Unsupervised Motion Representation Learning with Capsule Autoencoders](https://mlsanity.com/paper/2110.00529/) (2021)
- [Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble](https://mlsanity.com/paper/2110.06161/) (2021)
- [Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks](https://mlsanity.com/paper/2109.12946/) (2021)
- [Holistic Representation Learning for Multitask Trajectory Anomaly Detection](https://mlsanity.com/paper/2311.01851/) (2023)
- [A Comparative Analysis of Decision-Level Fusion for Multimodal Driver Behaviour Understanding](https://mlsanity.com/paper/2204.04734/) (2022)
- [CrossGLG: LLM Guides One-shot Skeleton-based 3D Action Recognition in a Cross-level Manner](https://mlsanity.com/paper/2403.10082/) (2024)
- [Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module](https://mlsanity.com/paper/2303.04346/) (2023)
- [Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion Network for Action Recognition](https://mlsanity.com/paper/2202.11374/) (2022)
- [Action Segmentation Using 2D Skeleton Heatmaps and Multi-Modality Fusion](https://mlsanity.com/paper/2309.06462/) (2023)
- [VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG](https://mlsanity.com/paper/2311.14775/) (2023)
- [IMoRe: Implicit Program-Guided Reasoning for Human Motion Q&A](https://mlsanity.com/paper/2508.01984/) (2025)
- [ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization](https://mlsanity.com/paper/2208.09414/) (2022)
- [Deep video representation learning: a survey](https://mlsanity.com/paper/2405.06574/) (2024)
- [Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition](https://mlsanity.com/paper/2503.20436/) (2025)
- [A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian Mutation](https://mlsanity.com/paper/2201.05314/) (2022)
- [Learning by Aligning 2D Skeleton Sequences and Multi-Modality Fusion](https://mlsanity.com/paper/2305.19480/) (2023)
- [Bottom-up approaches for multi-person pose estimation and it's applications: A brief review](https://mlsanity.com/paper/2112.11834/) (2021)
- [Subequivariant Reinforcement Learning Framework for Coordinated Motion Control](https://mlsanity.com/paper/2403.15100/) (2024)
- [Rodrigues Network for Learning Robot Actions](https://mlsanity.com/paper/2506.02618/) (2025)
- [Position and Orientation-Aware One-Shot Learning for Medical Action Recognition from Signal Data](https://mlsanity.com/paper/2309.15635/) (2023)
- [SigFormer: Sparse Signal-Guided Transformer for Multi-Modal Human Action Segmentation](https://mlsanity.com/paper/2311.17428/) (2023)
- [Cross-Block Fine-Grained Semantic Cascade for Skeleton-Based Sports Action Recognition](https://mlsanity.com/paper/2404.19383/) (2024)
- [SkelMamba: A State Space Model for Efficient Skeleton Action Recognition of Neurological Disorders](https://mlsanity.com/paper/2411.19544/) (2024)
- [RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks](https://mlsanity.com/paper/2108.07481/) (2021)

## Abstract (arXiv)

Skeleton-based action recognition has attracted research attentions in recent years. One common drawback in currently popular skeleton-based human action recognition methods is that the sparse skeleton information alone is not sufficient to fully characterize human motion. This limitation makes several existing methods incapable of correctly classifying action categories which exhibit only subtle motion differences. In this paper, we propose a novel framework for employing human pose skeleton and joint-centered light-weight information jointly in a two-stream graph convolutional network, namely, JOLO-GCN. Specifically, we use Joint-aligned optical Flow Patches (JFP) to capture the local subtle motion around each joint as the pivotal joint-centered visual information. Compared to the pure skeleton-based baseline, this hybrid scheme effectively boosts performance, while keeping the computational and memory overheads low. Experiments on the NTU RGB+D, NTU RGB+D 120, and the Kinetics-Skeleton dataset demonstrate clear accuracy improvements attained by the proposed method over the state-of-the-art skeleton-based methods.