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R-FCN: Object Detection via Region-based Fully Convolutional Networks

arXiv 1605.06409 ↗First posted 20 May 2016cs.CVcited by 414

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Abstract (arXiv)

We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image. To achieve this goal, we propose position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection. Our method can thus naturally adopt fully convolutional image classifier backbones, such as the latest Residual Networks (ResNets), for object detection. We show competitive results on the PASCAL VOC datasets (e.g., 83.6% mAP on the 2007 set) with the 101-layer ResNet. Meanwhile, our result is achieved at a test-time speed of 170ms per image, 2.5-20x faster than the Faster R-CNN counterpart. Code is made publicly available at: https://github.com/daijifeng001/r-fcn

Key facts. Cited by 414 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2016.

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Citing papers per year: 2021: 157; 2022: 94; 2023: 73; 2024: 55; 2025: 26; 2026: 9.

414 papers.

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  1. 2021Grounded Language-Image Pre-trainingcited by 1,147
  2. 2022GLIPv2: Unifying Localization and Vision-Language Understandingcited by 275
  3. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  4. 2021Dynamic Head: Unifying Object Detection Heads with Attentionscited by 185
  5. 2022DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detectioncited by 176
  6. 2022Compute Trends Across Three Eras of Machine Learningcited by 161
  7. 2021IntentNet: Learning to Predict Intention from Raw Sensor Datacited by 158
  8. 2021Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Losscited by 156
  9. 2021Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformercited by 134
  10. 2021Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergencecited by 130
  11. 2021Efficient DETR: Improving End-to-End Object Detector with Dense Priorcited by 128
  12. 2021Hierarchical Aggregation for 3D Instance Segmentationcited by 126
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  1. 2021Cross-Domain Adaptive Teacher for Object Detectioncited by 125
  2. 2021You Only Look One-level Featurecited by 116
  3. 2021RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detectioncited by 115
  4. 2021Instant-Teaching: An End-to-End Semi-Supervised Object Detection Frameworkcited by 112
  5. 2021DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detectioncited by 106
  6. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  7. 2021Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsitycited by 98
  8. 2021A Survey of Modern Deep Learning based Object Detection Modelscited by 97
  9. 2021Boundary Proposal Network for Two-Stage Natural Language Video Localizationcited by 88
  10. 2021Iterative Filter Adaptive Network for Single Image Defocus Deblurringcited by 85
  11. 2021YOLOP: You Only Look Once for Panoptic Driving Perceptioncited by 82
  12. 2021Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detectioncited by 79
  13. 2022TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformerscited by 78
  14. 2021Open-World Entity Segmentationcited by 74
  15. 2021SiamMOT: Siamese Multi-Object Trackingcited by 62
  16. 2021Locate and Label: A Two-stage Identifier for Nested Named Entity Recognitioncited by 60
  17. 2021Uncertainty-Aware Unsupervised Domain Adaptation in Object Detectioncited by 59
  18. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  19. 2021DEFT: Detection Embeddings for Trackingcited by 56
  20. 2021Adaptive Class Suppression Loss for Long-Tail Object Detectioncited by 55
  21. 2021Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detectioncited by 48
  22. 2021Temporal RoI Align for Video Object Recognitioncited by 44
  23. 2022Label Matching Semi-Supervised Object Detectioncited by 44
  24. 2022Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detectioncited by 43
  25. 2023Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modelingcited by 41
  26. 2021End-to-End Video Object Detection with Spatial-Temporal Transformerscited by 40
  27. 2023YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detectioncited by 33
  28. 2021Consistency-based Active Learning for Object Detectioncited by 32
  29. 2022A Data-Based Perspective on Transfer Learningcited by 32
  30. 2021Instance-wise Occlusion and Depth Orders in Natural Scenescited by 31
  31. 2023Griffon: Spelling out All Object Locations at Any Granularity with Large Language Modelscited by 31
  32. 2021Self-Supervised Representation Learning from Flow Equivariancecited by 28
  33. 2022ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixingcited by 28
  34. 2023Follow Anything: Open-set detection, tracking, and following in real-timecited by 28
  35. 2021PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyondcited by 27
  36. 2021Natural Language Video Localization with Learnable Moment Proposalscited by 27
  37. 2022Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challengecited by 27
  38. 2021Semantics-Guided Contrastive Network for Zero-Shot Object detectioncited by 26

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