mlsanityData to 7 Oct 2026

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

arXiv 2112.03328 ↗First posted 6 Dec 2021cs.CVcited by 20

Not on the map. Why?

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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 63 of its 130 references that are in the map. First posted on arXiv 6 Dec 2021.

Builds on

63 of 130 references.

References · 63

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2017Mask R-CNNcited by 7,259
  5. 2016Densely Connected Convolutional Networkscited by 6,691
  6. 2017Inductive Representation Learning on Large Graphscited by 6,284
  7. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  8. 2014Going Deeper with Convolutionscited by 4,231
  9. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  10. 2015Fast R-CNNcited by 2,930
  11. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  12. 2017Improved Training of Wasserstein GANscited by 2,223
38 more
  1. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  2. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  3. 2019Simplifying Graph Convolutional Networkscited by 1,470
  4. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
  5. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  6. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
  7. 2016Temporal Segment Networks: Towards Good Practices for Deep Action Recognitioncited by 899
  8. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
  9. 2017Learning Combinatorial Optimization Algorithms over Graphscited by 557
  10. 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
  11. 2018Deep Learning on Graphs: A Surveycited by 393
  12. 2018Neural Relational Inference for Interacting Systemscited by 339
  13. 2016Learning Convolutional Neural Networks for Graphscited by 314
  14. 2015Unitary Evolution Recurrent Neural Networkscited by 291
  15. 2015Diffusion-Convolutional Neural Networkscited by 268
  16. 2015Deep Convolutional Networks on Graph-Structured Datacited by 246
  17. 2019Learning Discrete Structures for Graph Neural Networkscited by 228
  18. 2017CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filterscited by 203
  19. 2019What graph neural networks cannot learn: depth vs widthcited by 195
  20. 2014Recurrent Neural Network Regularizationcited by 189
  21. 2019Graph Neural Networks with convolutional ARMA filterscited by 185
  22. 2018Adaptive Sampling Towards Fast Graph Representation Learningcited by 180
  23. 2018GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphscited by 164
  24. 2018Adaptive Graph Convolutional Neural Networkscited by 155
  25. 2016How to learn a graph from smooth signalscited by 146
  26. 2016Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognitioncited by 142
  27. 2016An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Datacited by 126
  28. 2018Learning graphs from data: A signal representation perspectivecited by 119
  29. 2018Large-Scale Learnable Graph Convolutional Networkscited by 113
  30. 2017View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Datacited by 105
  31. 2017Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networkscited by 92
  32. 2016Full-Capacity Unitary Recurrent Neural Networkscited by 90
  33. 2017On orthogonality and learning recurrent networks with long term dependenciescited by 82
  34. 2016Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networkscited by 63
  35. 2017Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networkscited by 62
  36. 2018Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSPcited by 58
  37. 2016Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflectionscited by 56
  38. 2019A Unified Framework for Structured Graph Learning via Spectral Constraintscited by 53

50 most cited of 63 All 63 (JSON)

Built on by

Cited by year

Citing papers per year: 2022: 7; 2023: 5; 2024: 3; 2025: 5.

20 papers.

Sort:

  1. 2022Efficient and Accurate Skeleton-Based Two-Person Interaction Recognition Using Inter- and Intra-body Graphscited by 2
  2. 2022Reinforcement-based frugal learning for satellite image change detectioncited by 1
  3. 2023Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detectioncited by 1
  4. 2025Multi-label Classification with Panoptic Context Aggregation Networkscited by 1
  5. 2022Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detectioncited by 0
  6. 2022Lightweight Graph Convolutional Networks with Topologically Consistent Magnitude Pruningcited by 0
  7. 2022Frugal Reinforcement-based Active Learningcited by 0
  8. 2022Training Lightweight Graph Convolutional Networks with Phase-field Modelscited by 0
  9. 2022Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detectioncited by 0
  10. 2023Budget-Aware Graph Convolutional Network Design using Probabilistic Magnitude Pruningcited by 0
  11. 2023Miniaturized Graph Convolutional Networks with Topologically Consistent Pruningcited by 0
  12. 2023Frugal Satellite Image Change Detection with Deep-Net Inversioncited by 0
8 more
  1. 2023One-Shot Multi-Rate Pruning of Graph Convolutional Networkscited by 0
  2. 2024Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognitioncited by 0
  3. 2024Multi-label Classification using Deep Multi-order Context-aware Kernel Networkscited by 0
  4. 2024Image Classification with Deep Reinforcement Active Learningcited by 0
  5. 2025Label-frugal satellite image change detection with generative virtual exemplar learningcited by 0
  6. 2025Image augmentation with invertible networks in interactive satellite image change detectioncited by 0
  7. 2025Label-Efficient Skeleton-based Recognition with Stable-Invertible Graph Convolutional Networkscited by 0
  8. 2025Active Learning for GCN-based Action Recognitioncited by 0