# Feedback Graph Convolutional Network for Skeleton-based Action Recognition

arXiv: https://arxiv.org/abs/2003.07564 · HTML: https://mlsanity.com/paper/2003.07564/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 22 of 22)

- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition](https://mlsanity.com/paper/2210.05895/) (2022)
- [Text-controlled Motion Mamba: Text-Instructed Temporal Grounding of Human Motion](https://mlsanity.com/paper/2404.11375/) (2024)
- [Skeleton-based Action Recognition via Temporal-Channel Aggregation](https://mlsanity.com/paper/2205.15936/) (2022)
- [Human Interaction Recognition Framework based on Interacting Body Part Attention](https://mlsanity.com/paper/2101.08967/) (2021)
- [Deep learning for 3D human pose estimation and mesh recovery: A survey](https://mlsanity.com/paper/2402.18844/) (2024)
- [A Decoupled Spatio-Temporal Framework for Skeleton-based Action Segmentation](https://mlsanity.com/paper/2312.05830/) (2023)
- [Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video Surveillance](https://mlsanity.com/paper/2201.05739/) (2022)
- [Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social Behaviour](https://mlsanity.com/paper/2208.03819/) (2022)
- [Token Boosting for Robust Self-Supervised Visual Transformer Pre-training](https://mlsanity.com/paper/2304.04175/) (2023)
- [MPM: A Unified 2D-3D Human Pose Representation via Masked Pose Modeling](https://mlsanity.com/paper/2306.17201/) (2023)
- [Deep video representation learning: a survey](https://mlsanity.com/paper/2405.06574/) (2024)
- [DD-GCN: Directed Diffusion Graph Convolutional Network for Skeleton-based Human Action Recognition](https://mlsanity.com/paper/2308.12501/) (2023)
- [DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology Modeling](https://mlsanity.com/paper/2501.12086/) (2025)
- [Action Recognition in Real-World Ambient Assisted Living Environment](https://mlsanity.com/paper/2503.23214/) (2025)
- [Efficient Spatialtemporal Context Modeling for Action Recognition](https://mlsanity.com/paper/2103.11190/) (2021)
- [Multi-view Graph Convolutional Networks with Differentiable Node Selection](https://mlsanity.com/paper/2212.05124/) (2022)
- [A New Adjacency Matrix Configuration in GCN-based Models for Skeleton-based Action Recognition](https://mlsanity.com/paper/2206.14344/) (2022)
- [HFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2501.11007/) (2025)
- [A Review on Coarse to Fine-Grained Animal Action Recognition](https://mlsanity.com/paper/2506.01214/) (2025)
- [A Topology-Aware Graph Convolutional Network for Human Pose Similarity and Action Quality Assessment](https://mlsanity.com/paper/2511.01194/) (2025)
- [TSkel-Mamba: Temporal Dynamic Modeling via State Space Model for Human Skeleton-based Action Recognition](https://mlsanity.com/paper/2512.11503/) (2025)

## Abstract (arXiv)

Skeleton-based action recognition has attracted considerable attention in computer vision since skeleton data is more robust to the dynamic circumstance and complicated background than other modalities. Recently, many researchers have used the Graph Convolutional Network (GCN) to model spatial-temporal features of skeleton sequences by an end-to-end optimization. However, conventional GCNs are feedforward networks which are impossible for low-level layers to access semantic information in the high-level layers. In this paper, we propose a novel network, named Feedback Graph Convolutional Network (FGCN). This is the first work that introduces the feedback mechanism into GCNs and action recognition. Compared with conventional GCNs, FGCN has the following advantages: (1) a multi-stage temporal sampling strategy is designed to extract spatial-temporal features for action recognition in a coarse-to-fine progressive process; (2) A dense connections based Feedback Graph Convolutional Block (FGCB) is proposed to introduce feedback connections into the GCNs. It transmits the high-level semantic features to the low-level layers and flows temporal information stage by stage to progressively model global spatial-temporal features for action recognition; (3) The FGCN model provides early predictions. In the early stages, the model receives partial information about actions. Naturally, its predictions are relatively coarse. The coarse predictions are treated as the prior to guide the feature learning of later stages for a accurate prediction. Extensive experiments on the datasets, NTU-RGB+D, NTU-RGB+D120 and Northwestern-UCLA, demonstrate that the proposed FGCN is effective for action recognition. It achieves the state-of-the-art performance on the three datasets.