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Graph R-CNN for Scene Graph Generation

arXiv 1808.00191 ↗First posted 1 Aug 2018cs.CV cs.LGcited by 296

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Abstract (arXiv)

We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images. Our model contains a Relation Proposal Network (RePN) that efficiently deals with the quadratic number of potential relations between objects in an image. We also propose an attentional Graph Convolutional Network (aGCN) that effectively captures contextual information between objects and relations. Finally, we introduce a new evaluation metric that is more holistic and realistic than existing metrics. We report state-of-the-art performance on scene graph generation as evaluated using both existing and our proposed metrics.

Key facts. Cited by 296 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Aug 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 73; 2022: 82; 2023: 62; 2024: 36; 2025: 27; 2026: 16.

296 papers.

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  1. 2022Vision GNN: An Image is Worth Graph of Nodescited by 185
  2. 2021SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequencescited by 159
  3. 2021Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generationcited by 137
  4. 2021A Comprehensive Survey of Scene Graphs: Generation and Applicationcited by 125
  5. 2022Panoptic Scene Graph Generationcited by 114
  6. 2021Energy-Based Learning for Scene Graph Generationcited by 104
  7. 2021SGTR: End-to-end Scene Graph Generation with Transformercited by 102
  8. 2021Spatial-Temporal Transformer for Dynamic Scene Graph Generationcited by 95
  9. 2022RelTR: Relation Transformer for Scene Graph Generationcited by 95
  10. 2021Holistic 3D Scene Understanding from a Single Image with Implicit Representationcited by 94
  11. 2021Chest ImaGenome Dataset for Clinical Reasoningcited by 89
  12. 2021Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphscited by 83
38 more
  1. 2024Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationshipscited by 76
  2. 2021SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanismcited by 75
  3. 2022Multi-Modal Knowledge Graph Construction and Application: A Surveycited by 74
  4. 2021Graph-MLP: Node Classification without Message Passing in Graphcited by 73
  5. 2022Scene Graph Generation: A Comprehensive Surveycited by 73
  6. 2022A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directionscited by 73
  7. 2021Recovering the Unbiased Scene Graphs from the Biased Onescited by 71
  8. 2022Relationformer: A Unified Framework for Image-to-Graph Generationcited by 67
  9. 2021Learning of Visual Relations: The Devil is in the Tailscited by 65
  10. 2021Learning to Generate Scene Graph from Natural Language Supervisioncited by 64
  11. 2022The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generationcited by 60
  12. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  13. 2022Instance Relation Graph Guided Source-Free Domain Adaptive Object Detectioncited by 55
  14. 2025Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spacescited by 53
  15. 2021Target Adaptive Context Aggregation for Video Scene Graph Generationcited by 51
  16. 2021Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networkscited by 50
  17. 2024RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulationcited by 48
  18. 2021Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysiscited by 47
  19. 2023Panoptic Video Scene Graph Generationcited by 46
  20. 2022ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classificationcited by 45
  21. 2024EGTR: Extracting Graph from Transformer for Scene Graph Generationcited by 45
  22. 2021Structured Sparse R-CNN for Direct Scene Graph Generationcited by 44
  23. 2023RLIPv2: Fast Scaling of Relational Language-Image Pre-trainingcited by 44
  24. 2023Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphscited by 44
  25. 2022Deep Learning Approaches on Image Captioning: A Reviewcited by 43
  26. 2023VL-SAT: Visual-Linguistic Semantics Assisted Training for 3D Semantic Scene Graph Prediction in Point Cloudcited by 42
  27. 2022Continuous Scene Representations for Embodied AIcited by 39
  28. 2023Incremental 3D Semantic Scene Graph Prediction from RGB Sequencescited by 39
  29. 2021Unified Graph Structured Models for Video Understandingcited by 38
  30. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  31. 2022Latent Graph Representations for Critical View of Safety Assessmentcited by 37
  32. 2023Prototype-based Embedding Network for Scene Graph Generationcited by 37
  33. 2023Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representationscited by 37
  34. 2021MAAS: Multi-modal Assignation for Active Speaker Detectioncited by 35
  35. 2021Probabilistic Modeling of Semantic Ambiguity for Scene Graph Generationcited by 35
  36. 2022Contrastive Language-Image Pre-Training with Knowledge Graphscited by 34
  37. 2021DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimizationcited by 33
  38. 2021In Defense of Scene Graphs for Image Captioningcited by 31

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