mlsanityData to 7 Oct 2026

You Only Look One-level Feature

arXiv 2103.09460 ↗First posted 17 Mar 2021cs.CVcited by 116

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Abstract (arXiv)

This paper revisits feature pyramids networks (FPN) for one-stage detectors and points out that the success of FPN is due to its divide-and-conquer solution to the optimization problem in object detection rather than multi-scale feature fusion. From the perspective of optimization, we introduce an alternative way to address the problem instead of adopting the complex feature pyramids - em utilizing only one-level feature for detection. Based on the simple and efficient solution, we present You Only Look One-level Feature (YOLOF). In our method, two key components, Dilated Encoder and Uniform Matching, are proposed and bring considerable improvements. Extensive experiments on the COCO benchmark prove the effectiveness of the proposed model. Our YOLOF achieves comparable results with its feature pyramids counterpart RetinaNet while being 2.5× faster. Without transformer layers, YOLOF can match the performance of DETR in a single-level feature manner with 7× less training epochs. With an image size of 608×608, YOLOF achieves 44.3 mAP running at 60 fps on 2080Ti, which is 13% faster than YOLOv4. Code is available at urlhttps://github.com/megvii-model/YOLOF.

Key facts. Cited by 116 papers in the map (data through 7 Oct 2026). Builds on 45 of its 50 references that are in the map. First posted on arXiv 17 Mar 2021.

Builds on

45 of 50 references.

References · 45

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2020End-to-End Object Detection with Transformerscited by 7,384
  6. 2017Mask R-CNNcited by 7,259
  7. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  8. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  9. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  10. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  11. 2015Fast R-CNNcited by 2,930
  12. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
33 more
  1. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  2. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  3. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  4. 2018Group Normalizationcited by 1,514
  5. 2017Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourcited by 1,479
  6. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  7. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,361
  8. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,275
  9. 2019Objects as Pointscited by 1,172
  10. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,128
  11. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  12. 2018Path Aggregation Network for Instance Segmentationcited by 796
  13. 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
  14. 2017Focal Loss for Dense Object Detectioncited by 749
  15. 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
  16. 2019Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selectioncited by 539
  17. 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 515
  18. 2019Hybrid Task Cascade for Instance Segmentationcited by 479
  19. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  20. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  21. 2018Context Encoding for Semantic Segmentationcited by 289
  22. 2019NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detectioncited by 289
  23. 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
  24. 2019Scale-Aware Trident Networks for Object Detectioncited by 159
  25. 2020Probabilistic Anchor Assignment with IoU Prediction for Object Detectioncited by 150
  26. 2015End-to-end people detection in crowded scenescited by 140
  27. 2017An Analysis of Scale Invariance in Object Detection - SNIPcited by 119
  28. 2020TIDE: A General Toolbox for Identifying Object Detection Errorscited by 113
  29. 2020AutoAssign: Differentiable Label Assignment for Dense Object Detectioncited by 100
  30. 2019FreeAnchor: Learning to Match Anchors for Visual Object Detectioncited by 90
  31. 2018SNIPER: Efficient Multi-Scale Trainingcited by 87
  32. 2018Deep Feature Pyramid Reconfiguration for Object Detectioncited by 23
  33. 2020SWA Object Detectioncited by 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 34; 2023: 24; 2024: 21; 2025: 14; 2026: 10.

116 papers.

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  1. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
  2. 2022Exploring Plain Vision Transformer Backbones for Object Detectioncited by 723
  3. 2022Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignmentcited by 96
  4. 2022Rethinking Efficient Lane Detection via Curve Modelingcited by 87
  5. 2022Sparse Instance Activation for Real-Time Instance Segmentationcited by 63
  6. 2022PODA: Prompt-driven Zero-shot Domain Adaptationcited by 60
  7. 2023PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Cloudscited by 54
  8. 2021RelationTrack: Relation-aware Multiple Object Tracking with Decoupled Representationcited by 52
  9. 2022RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detectioncited by 51
  10. 2023Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanismcited by 51
  11. 2022NMS Strikes Backcited by 33
  12. 2021FOVEA: Foveated Image Magnification for Autonomous Navigationcited by 31
38 more
  1. 2021Anchor DETR: Query Design for Transformer-Based Object Detectioncited by 30
  2. 2023Point2RBox: Combine Knowledge from Synthetic Visual Patterns for End-to-end Oriented Object Detection with Single Point Supervisioncited by 21
  3. 2022Efficient Visual Computing with Camera RAW Snapshotscited by 18
  4. 2021Improving Object Detection by Label Assignment Distillationcited by 17
  5. 2022REAP: A Large-Scale Realistic Adversarial Patch Benchmarkcited by 17
  6. 2021MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detectioncited by 16
  7. 2023MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detectioncited by 15
  8. 2022A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillancecited by 14
  9. 2022Centralized Feature Pyramid for Object Detectioncited by 13
  10. 2022HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectorscited by 12
  11. 2022You Should Look at All Objectscited by 12
  12. 2022Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center studycited by 10
  13. 2022Optimal Correction Cost for Object Detection Evaluationcited by 10
  14. 2023Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregationcited by 10
  15. 2023MPDIoU: A Loss for Efficient and Accurate Bounding Box Regressioncited by 10
  16. 2025USIS16K: High-Quality Dataset for Underwater Salient Instance Segmentationcited by 10
  17. 2024Background Semantics Matter: Cross-Task Feature Exchange Network for Clustered Infrared Small Target Detectioncited by 9
  18. 2022Deep Learning for Logo Detection: A Surveycited by 8
  19. 2023Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolutioncited by 8
  20. 2024ELA: Efficient Local Attention for Deep Convolutional Neural Networkscited by 8
  21. 2024DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR Imagescited by 8
  22. 2025AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysiscited by 7
  23. 2026Distilled Large Language Model-Driven Dynamic Sparse Expert Activation Mechanismcited by 7
  24. 2022BoxMask: Revisiting Bounding Box Supervision for Video Object Detectioncited by 6
  25. 2022PAI3D: Painting Adaptive Instance-Prior for 3D Object Detectioncited by 6
  26. 2023X-MLP: A Patch Embedding-Free MLP Architecture for Visioncited by 6
  27. 2024YOLO-TS: Real-Time Traffic Sign Detection with Enhanced Accuracy Using Optimized Receptive Fields and Anchor-Free Fusioncited by 6
  28. 2025Wholly-WOOD: Wholly Leveraging Diversified-quality Labels for Weakly-supervised Oriented Object Detectioncited by 6
  29. 2021TE-YOLOF: Tiny and efficient YOLOF for blood cell detectioncited by 5
  30. 2021SDTP: Semantic-aware Decoupled Transformer Pyramid for Dense Image Predictioncited by 5
  31. 2022Efficient Decoder-free Object Detection with Transformerscited by 5
  32. 2022Surround-view Fisheye BEV-Perception for Valet Parking: Dataset, Baseline and Distortion-insensitive Multi-task Frameworkcited by 4
  33. 2023YOLOCS: Object Detection based on Dense Channel Compression for Feature Spatial Solidificationcited by 4
  34. 2023Two-stage optimized unified adversarial patch for attacking visible-infrared cross-modal detectors in the physical worldcited by 4
  35. 2024CNCA: Toward Customizable and Natural Generation of Adversarial Camouflage for Vehicle Detectorscited by 4
  36. 2025SparseMeXT Unlocking the Potential of Sparse Representations for HD Map Constructioncited by 4
  37. 2025Visual Detector Compression via Location-Aware Discriminant Analysiscited by 4
  38. 2021Mask or Non-Mask? Robust Face Mask Detector via Triplet-Consistency Representation Learningcited by 3

50 most cited of 116 All 116 (JSON)