mlsanityData to 7 Oct 2026

DATE: Dual Assignment for End-to-End Fully Convolutional Object Detection

arXiv 2211.13859 ↗First posted 25 Nov 2022cs.CVcited by 4

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Abstract (arXiv)

Fully convolutional detectors discard the one-to-many assignment and adopt a one-to-one assigning strategy to achieve end-to-end detection but suffer from the slow convergence issue. In this paper, we revisit these two assignment methods and find that bringing one-to-many assignment back to end-to-end fully convolutional detectors helps with model convergence. Based on this observation, we propose em Dual Assignment for end-to-end fully convolutional deTEction (DATE). Our method constructs two branches with one-to-many and one-to-one assignment during training and speeds up the convergence of the one-to-one assignment branch by providing more supervision signals. DATE only uses the branch with the one-to-one matching strategy for model inference, which doesn't bring inference overhead. Experimental results show that Dual Assignment gives nontrivial improvements and speeds up model convergence upon OneNet and DeFCN. Code: https://github.com/YiqunChen1999/date.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 41 of its 50 references that are in the map. First posted on arXiv 25 Nov 2022.

Builds on

41 of 50 references.

References · 41

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  5. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  6. 2020End-to-End Object Detection with Transformerscited by 7,384
  7. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  8. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  9. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  10. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  11. 2015Fast R-CNNcited by 2,930
  12. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
29 more
  1. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  2. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  3. 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,379
  4. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,361
  5. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,275
  6. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  7. 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
  8. 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
  9. 2019Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selectioncited by 539
  10. 2022DN-DETR: Accelerate DETR Training by Introducing Query DeNoisingcited by 486
  11. 2021Conditional DETR for Fast Training Convergencecited by 422
  12. 2020Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detectioncited by 408
  13. 2017Relation Networks for Object Detectioncited by 305
  14. 2018CrowdHuman: A Benchmark for Detecting Human in a Crowdcited by 283
  15. 2019RepPoints: Point Set Representation for Object Detectioncited by 264
  16. 2021TOOD: Task-aligned One-stage Object Detectioncited by 218
  17. 2022DETRs with Hybrid Matchingcited by 149
  18. 2015End-to-end people detection in crowded scenescited by 140
  19. 2021Efficient DETR: Improving End-to-End Object Detector with Dense Priorcited by 128
  20. 2017Learning non-maximum suppressioncited by 112
  21. 2020AutoAssign: Differentiable Label Assignment for Dense Object Detectioncited by 100
  22. 2020End-to-End Object Detection with Fully Convolutional Networkcited by 70
  23. 2022Accelerating DETR Convergence via Semantic-Aligned Matchingcited by 58
  24. 2021PnP-DETR: Towards Efficient Visual Analysis with Transformerscited by 52
  25. 2020What Makes for End-to-End Object Detection?cited by 47
  26. 2020RepPoints V2: Verification Meets Regression for Object Detectioncited by 40
  27. 2016DeepSetNet: Predicting Sets with Deep Neural Networkscited by 15
  28. 2019Elucidating image-to-set prediction: An analysis of models, losses and datasetscited by 10
  29. 2015Learning to decompose for object detection and instance segmentationcited by 4

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Cited by year

Citing papers per year: 2023: 1; 2024: 3.

4 papers.

  1. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 508
  2. 2023LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Networkcited by 8
  3. 2024YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systemscited by 6
  4. 2024Polar R-CNN: End-to-End Lane Detection with Fewer Anchorscited by 1