# Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition

arXiv: https://arxiv.org/abs/2112.03328 · HTML: https://mlsanity.com/paper/2112.03328/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 63 of its 130 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 30 of 63)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://mlsanity.com/paper/1611.08402/) (2016)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [Neural Relational Inference for Interacting Systems](https://mlsanity.com/paper/1802.04687/) (2018)
- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [Unitary Evolution Recurrent Neural Networks](https://mlsanity.com/paper/1511.06464/) (2015)
- [Diffusion-Convolutional Neural Networks](https://mlsanity.com/paper/1511.02136/) (2015)
- [Deep Convolutional Networks on Graph-Structured Data](https://mlsanity.com/paper/1506.05163/) (2015)
- [Learning Discrete Structures for Graph Neural Networks](https://mlsanity.com/paper/1903.11960/) (2019)
- [CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters](https://mlsanity.com/paper/1705.07664/) (2017)

## Built on by (most cited first, 20 of 20)

- [Efficient and Accurate Skeleton-Based Two-Person Interaction Recognition Using Inter- and Intra-body Graphs](https://mlsanity.com/paper/2207.12648/) (2022)
- [Reinforcement-based frugal learning for satellite image change detection](https://mlsanity.com/paper/2203.11564/) (2022)
- [Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection](https://mlsanity.com/paper/2312.16965/) (2023)
- [Multi-label Classification with Panoptic Context Aggregation Networks](https://mlsanity.com/paper/2512.23486/) (2025)
- [Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection](https://mlsanity.com/paper/2203.11559/) (2022)
- [Lightweight Graph Convolutional Networks with Topologically Consistent Magnitude Pruning](https://mlsanity.com/paper/2203.13616/) (2022)
- [Frugal Reinforcement-based Active Learning](https://mlsanity.com/paper/2212.04868/) (2022)
- [Training Lightweight Graph Convolutional Networks with Phase-field Models](https://mlsanity.com/paper/2212.09415/) (2022)
- [Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection](https://mlsanity.com/paper/2212.13974/) (2022)
- [Budget-Aware Graph Convolutional Network Design using Probabilistic Magnitude Pruning](https://mlsanity.com/paper/2305.19343/) (2023)
- [Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning](https://mlsanity.com/paper/2306.17590/) (2023)
- [Frugal Satellite Image Change Detection with Deep-Net Inversion](https://mlsanity.com/paper/2309.14781/) (2023)
- [One-Shot Multi-Rate Pruning of Graph Convolutional Networks](https://mlsanity.com/paper/2312.17615/) (2023)
- [Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition](https://mlsanity.com/paper/2412.11813/) (2024)
- [Multi-label Classification using Deep Multi-order Context-aware Kernel Networks](https://mlsanity.com/paper/2412.19491/) (2024)
- [Image Classification with Deep Reinforcement Active Learning](https://mlsanity.com/paper/2412.19877/) (2024)
- [Label-frugal satellite image change detection with generative virtual exemplar learning](https://mlsanity.com/paper/2510.06926/) (2025)
- [Image augmentation with invertible networks in interactive satellite image change detection](https://mlsanity.com/paper/2510.18660/) (2025)
- [Label-Efficient Skeleton-based Recognition with Stable-Invertible Graph Convolutional Networks](https://mlsanity.com/paper/2511.17345/) (2025)
- [Active Learning for GCN-based Action Recognition](https://mlsanity.com/paper/2511.21625/) (2025)

## Abstract (arXiv)

Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing convolutional operations to arbitrary non-regular domains. In particular, GCNs operating on spatial domains show superior performances compared to spectral ones, however their success is highly dependent on how the topology of input graphs is defined. In this paper, we introduce a novel framework for graph convolutional networks that learns the topological properties of graphs. The design principle of our method is based on the optimization of a constrained objective function which learns not only the usual convolutional parameters in GCNs but also a transformation basis that conveys the most relevant topological relationships in these graphs. Experiments conducted on the challenging task of skeleton-based action recognition shows the superiority of the proposed method compared to handcrafted graph design as well as the related work.