mlsanityData to 7 Oct 2026

A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective

arXiv 2209.13232 ↗First posted 27 Sep 2022cs.CV cs.AI cs.LGcited by 56

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Abstract (arXiv)

Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (e.g., social network analysis and recommender systems), computer vision (e.g., object detection and point cloud learning), and natural language processing (e.g., relation extraction and sequence learning), to name a few. With the emergence of Transformers in natural language processing and computer vision, graph Transformers embed a graph structure into the Transformer architecture to overcome the limitations of local neighborhood aggregation while avoiding strict structural inductive biases. In this paper, we present a comprehensive review of GNNs and graph Transformers in computer vision from a task-oriented perspective. Specifically, we divide their applications in computer vision into five categories according to the modality of input data, i.e., 2D natural images, videos, 3D data, vision + language, and medical images. In each category, we further divide the applications according to a set of vision tasks. Such a task-oriented taxonomy allows us to examine how each task is tackled by different GNN-based approaches and how well these approaches perform. Based on the necessary preliminaries, we provide the definitions and challenges of the tasks, in-depth coverage of the representative approaches, as well as discussions regarding insights, limitations, and future directions.

Key facts. Cited by 56 papers in the map (data through 7 Oct 2026). Builds on 260 of its 402 references that are in the map. First posted on arXiv 27 Sep 2022.

Builds on

260 of 402 references.

References · 260

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  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
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50 most cited of 260 All 260 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 12; 2024: 8; 2025: 29; 2026: 4.

56 papers.

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  1. 2023Attending to Graph Transformerscited by 81
  2. 2022On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networkscited by 63
  3. 2024Toward Visual Grounding: A Surveycited by 52
  4. 2024Graph Transformers: A Surveycited by 32
  5. 2023A Survey on Spectral Graph Neural Networkscited by 26
  6. 2023Over-Squashing in Graph Neural Networks: A Comprehensive surveycited by 23
  7. 2025Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunitiescited by 23
  8. 2022Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalizationcited by 20
  9. 2025Rethinking Query-based Transformer for Continual Image Segmentationcited by 14
  10. 2024Continual Learning on Graphs: A Surveycited by 11
  11. 2023Graph Neural Networks in Vision-Language Image Understanding: A Surveycited by 10
  12. 2023Privacy-Preserving Graph Machine Learning from Data to Computation: A Surveycited by 10
38 more
  1. 2024Learning by Watching: A Review of Video-based Learning Approaches for Robot Manipulationcited by 10
  2. 2023MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deploymentcited by 9
  3. 2025Why LVLMs Are More Prone to Hallucinations in Longer Responses: The Role of Contextcited by 9
  4. 2023Graph Neural Network for spatiotemporal data: methods and applicationscited by 6
  5. 2025ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioningcited by 6
  6. 2025Intervene-All-Paths: Unified Mitigation of LVLM Hallucinations across Alignment Formatscited by 6
  7. 2025The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logiccited by 5
  8. 2025Sim-DETR: Unlock DETR for Temporal Sentence Groundingcited by 4
  9. 2025Closed-Loop Transfer for Weakly-supervised Affordance Groundingcited by 4
  10. 2023Inductive biases in deep learning models for weather predictioncited by 3
  11. 2023Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Networkcited by 3
  12. 2024Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learningcited by 3
  13. 2025Aggregate-Combine-Readout GNNs Are More Expressive Than Logic C2cited by 3
  14. 2025No More Sibling Rivalry: Debiasing Human-Object Interaction Detectioncited by 3
  15. 2025Decoding the Surgical Scene: A Scoping Review of Scene Graphs in Surgerycited by 3
  16. 2023Learning to search for and detect objects in foveal images using deep learningcited by 2
  17. 2024GCV-Turbo: End-to-end Acceleration of GNN-based Computer Vision Tasks on FPGAcited by 2
  18. 2024Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learningcited by 2
  19. 2025GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completioncited by 2
  20. 2025Token Adaptation via Side Graph Convolution for Efficient Fine-tuning of 3D Point Cloud Transformerscited by 2
  21. 2025Learning Privacy from Visual Entitiescited by 2
  22. 2025Context-Aware Semantic Segmentation: Enhancing Pixel-Level Understanding with Large Language Models for Advanced Vision Applicationscited by 2
  23. 2025AdaptViG: Adaptive Vision GNN with Exponential Decay Gatingcited by 2
  24. 2023EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attentioncited by 1
  25. 2024ViGEO: an Assessment of Vision GNNs in Earth Observationcited by 1
  26. 2025KAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural Networkscited by 1
  27. 2025Hybrid State-Space and GRU-based Graph Tokenization Mamba for Hyperspectral Image Classificationcited by 1
  28. 2025Cross-Model Transferability of Adversarial Patches in Real-time Segmentation for Autonomous Drivingcited by 1
  29. 2025The Application of Transformer-Based Models for Predicting Consequences of Cyber Attackscited by 1
  30. 2025Augmenting Moment Retrieval: Zero-Dependency Two-Stage Learningcited by 1
  31. 2025A Re-node Self-training Approach for Deep Graph-based Semi-supervised Classification on Multi-view Image Datacited by 1
  32. 2025Contrastive Graph Modeling for Cross-Domain Few-Shot Medical Image Segmentationcited by 1
  33. 2022The Path to Autonomous Learnerscited by 0
  34. 2023Unsupervised Low Light Image Enhancement Using SNR-Aware Swin Transformercited by 0
  35. 2025A Split-Window Transformer for Multi-Model Sequence Spammer Detection using Multi-Model Variational Autoencodercited by 0
  36. 2025Mineral segmentation using electron microscope images and spectral sampling through multimodal graph neural networkscited by 0
  37. 2025Quantum Complex-Valued Self-Attention Modelcited by 0
  38. 2025Deep Learning For Point Cloud Denoising: A Surveycited by 0

50 most cited of 56 All 56 (JSON)