mlsanityData to 7 Oct 2026

Relation Networks for Object Detection

arXiv 1711.11575 ↗First posted 30 Nov 2017cs.CVcited by 305

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Abstract (arXiv)

Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector.

Key facts. Cited by 305 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Nov 2017.

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Citing papers per year: 2021: 110; 2022: 101; 2023: 33; 2024: 30; 2025: 14; 2026: 17.

305 papers.

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  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  2. 2021Is Space-Time Attention All You Need for Video Understanding?cited by 1,609
  3. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  4. 2021Swin Transformer V2: Scaling Up Capacity and Resolutioncited by 1,215
  5. 2021Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrievalcited by 1,175
  6. 2021Video Swin Transformercited by 1,090
  7. 2021Multiscale Vision Transformerscited by 896
  8. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 508
  9. 2021ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biasescited by 484
  10. 2021Bottleneck Transformers for Visual Recognitioncited by 364
  11. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  12. 2021End-to-End Semi-Supervised Object Detection with Soft Teachercited by 315
38 more
  1. 2021Attention Mechanisms in Computer Vision: A Surveycited by 232
  2. 2021Localizing Objects with Self-Supervised Transformers and no Labelscited by 227
  3. 2021Group-Free 3D Object Detection via Transformerscited by 215
  4. 2022Visual Attention Networkcited by 210
  5. 2021Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimationcited by 192
  6. 2021HOTR: End-to-End Human-Object Interaction Detection with Transformerscited by 183
  7. 2021You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detectioncited by 170
  8. 2023Towards Open Vocabulary Learning: A Surveycited by 165
  9. 2021Conformer: Local Features Coupling Global Representations for Visual Recognitioncited by 164
  10. 2021Exploring Temporal Coherence for More General Video Face Forgery Detectioncited by 158
  11. 2021Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Timecited by 140
  12. 2021Focal and Global Knowledge Distillation for Detectorscited by 138
  13. 2021Cross-Domain Adaptive Teacher for Object Detectioncited by 125
  14. 2021A Survey of Visual Transformerscited by 117
  15. 2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
  16. 2021Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Biascited by 115
  17. 2021Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identificationcited by 114
  18. 2022Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentationcited by 114
  19. 2021General Instance Distillation for Object Detectioncited by 113
  20. 2021Instant-Teaching: An End-to-End Semi-Supervised Object Detection Frameworkcited by 112
  21. 2021Beyond Self-attention: External Attention using Two Linear Layers for Visual Taskscited by 109
  22. 2021LambdaNetworks: Modeling Long-Range Interactions Without Attentioncited by 107
  23. 2021Distilling Object Detectors via Decoupled Featurescited by 105
  24. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  25. 2021Involution: Inverting the Inherence of Convolution for Visual Recognitioncited by 99
  26. 2022Global Context Vision Transformerscited by 98
  27. 2021Holistic 3D Scene Understanding from a Single Image with Implicit Representationcited by 94
  28. 2021CAT: Cross Attention in Vision Transformercited by 92
  29. 2021An Image is Worth 16x16 Words, What is a Video Worth?cited by 87
  30. 2021TVT: Transferable Vision Transformer for Unsupervised Domain Adaptationcited by 81
  31. 2022Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognitioncited by 81
  32. 2022TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformerscited by 78
  33. 20213D-MAN: 3D Multi-frame Attention Network for Object Detectioncited by 73
  34. 2022Scene Graph Generation: A Comprehensive Surveycited by 73
  35. 2021Multiple Object Tracking with Correlation Learningcited by 67
  36. 2021Multi-Person 3D Motion Prediction with Multi-Range Transformerscited by 63
  37. 2022Video Transformers: A Surveycited by 63
  38. 2022CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentationcited by 60

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