mlsanityData to 7 Oct 2026

Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection

arXiv 2312.13822 ↗First posted 21 Dec 2023cs.CVcited by 2

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Abstract (arXiv)

For object detection task with noisy labels, it is important to consider not only categorization noise, as in image classification, but also localization noise, missing annotations, and bogus bounding boxes. However, previous studies have only addressed certain types of noise (e.g., localization or categorization). In this paper, we propose Universal-Noise Annotation (UNA), a more practical setting that encompasses all types of noise that can occur in object detection, and analyze how UNA affects the performance of the detector. We analyzed the development direction of previous works of detection algorithms and examined the factors that impact the robustness of detection model learning method. We open-source the code for injecting UNA into the dataset and all the training log and weight are also shared.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 36 of its 50 references that are in the map. First posted on arXiv 21 Dec 2023.

Builds on

36 of 50 references.

References · 36

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  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. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  7. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  8. 2022A ConvNet for the 2020scited by 3,545
  9. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  10. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  11. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  12. 2017Deformable Convolutional Networkscited by 1,481
24 more
  1. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,306
  2. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 691
  3. 2018Deformable ConvNets v2: More Deformable, Better Resultscited by 666
  4. 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 577
  5. 2022DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETRcited by 569
  6. 2019Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selectioncited by 539
  7. 2021Conditional DETR for Fast Training Convergencecited by 422
  8. 2021End-to-End Semi-Supervised Object Detection with Soft Teachercited by 315
  9. 2018Joint Optimization Framework for Learning with Noisy Labelscited by 293
  10. 2017WebVision Database: Visual Learning and Understanding from Web Datacited by 262
  11. 2021Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotationscited by 253
  12. 2017CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noisecited by 181
  13. 2018Learning to Learn from Noisy Labeled Datacited by 160
  14. 2020TIDE: A General Toolbox for Identifying Object Detection Errorscited by 113
  15. 2021Augmentation Strategies for Learning with Noisy Labelscited by 86
  16. 2020Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noisecited by 82
  17. 2018Localization Recall Precision (LRP): A New Performance Metric for Object Detectioncited by 31
  18. 2020Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detectioncited by 29
  19. 2020Towards Noise-resistant Object Detection with Noisy Annotationscited by 24
  20. 2022Robust Object Detection With Inaccurate Bounding Boxescited by 20
  21. 2019Training Object Detectors With Noisy Datacited by 19
  22. 2020Object Detection as a Positive-Unlabeled Problemcited by 18
  23. 2020Semi-Supervised Object Detection with Sparsely Annotated Datasetcited by 10
  24. 2021Noisy Annotation Refinement for Object Detectioncited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024YOLO-FEDER FusionNet: A Novel Deep Learning Architecture for Drone Detectioncited by 4
  2. 2026Look Where It Matters: High-Resolution Crops Retrieval for Efficient VLMscited by 2