mlsanityData to 14 Oct 2026

GridMask Data Augmentation

arXiv 2001.04086 ↗First posted 13 Jan 2020cs.CVcited by 123

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Abstract (arXiv)

We propose a novel data augmentation method `GridMask' in this paper. It utilizes information removal to achieve state-of-the-art results in a variety of computer vision tasks. We analyze the requirement of information dropping. Then we show limitation of existing information dropping algorithms and propose our structured method, which is simple and yet very effective. It is based on the deletion of regions of the input image. Our extensive experiments show that our method outperforms the latest AutoAugment, which is way more computationally expensive due to the use of reinforcement learning to find the best policies. On the ImageNet dataset for recognition, COCO2017 object detection, and on Cityscapes dataset for semantic segmentation, our method all notably improves performance over baselines. The extensive experiments manifest the effectiveness and generality of the new method.

Key facts. Cited by 123 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Jan 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 20; 2022: 27; 2023: 20; 2024: 26; 2025: 19; 2026: 11.

123 papers.

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  1. 2021A Survey on Deep Semi-supervised Learningcited by 251
  2. 2021Mix3D: Out-of-Context Data Augmentation for 3D Scenescited by 126
  3. 2023PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentationcited by 115
  4. 2022A Comprehensive Survey of Image Augmentation Techniques for Deep Learningcited by 97
  5. 2022Image Data Augmentation for Deep Learning: A Surveycited by 96
  6. 2022Expanding Small-Scale Datasets with Guided Imaginationcited by 62
  7. 2021Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowdscited by 58
  8. 2023A-JEPA: Joint-Embedding Predictive Architecture Can Listencited by 53
  9. 2022TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformerscited by 48
  10. 2021When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarkscited by 41
  11. 2024A Comprehensive Survey on Data Augmentationcited by 40
  12. 2023Object as Query: Lifting any 2D Object Detector to 3D Detectioncited by 38
38 more
  1. 2022Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentationcited by 28
  2. 2022A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspectivecited by 27
  3. 2021Survey: Image Mixing and Deleting for Data Augmentationcited by 26
  4. 2021Scale-aware Automatic Augmentation for Object Detectioncited by 23
  5. 2023Image Data Augmentation Approaches: A Comprehensive Survey and Future directionscited by 23
  6. 2023FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D Detectioncited by 21
  7. 2024Distribution-Aware Data Expansion with Diffusion Modelscited by 20
  8. 2022Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentationcited by 19
  9. 2023What Can Human Sketches Do for Object Detection?cited by 19
  10. 2022A Simple Baseline for Multi-Camera 3D Object Detectioncited by 16
  11. 2022RecursiveMix: Mixed Learning with Historycited by 15
  12. 2022A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillancecited by 14
  13. 2022PoseTrans: A Simple Yet Effective Pose Transformation Augmentation for Human Pose Estimationcited by 14
  14. 2023Triplet Contrastive Representation Learning for Unsupervised Vehicle Re-identificationcited by 14
  15. 2024Automated data processing and feature engineering for deep learning and big data applications: a surveycited by 14
  16. 2021Mixup Without Hesitationcited by 13
  17. 2022Improving Robustness with Adaptive Weight Decaycited by 13
  18. 2024EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classificationcited by 13
  19. 2022TC-SKNet with GridMask for Low-complexity Classification of Acoustic scenecited by 12
  20. 2023Win-Win: Training High-Resolution Vision Transformers from Two Windowscited by 12
  21. 2021VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challengescited by 11
  22. 2023CLAP: Isolating Content from Style through Contrastive Learning with Augmented Promptscited by 10
  23. 2022VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challengescited by 8
  24. 2023MixBag: Bag-Level Data Augmentation for Learning from Label Proportionscited by 8
  25. 2024ColorMAE: Exploring data-independent masking strategies in Masked AutoEncoderscited by 8
  26. 2024ED4: Explicit Data-level Debiasing for Deepfake Detectioncited by 8
  27. 2022AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classificationcited by 7
  28. 2024AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentationcited by 7
  29. 2021ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentationcited by 6
  30. 2022A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptionscited by 6
  31. 2022Passage-Mask: A Learnable Regularization Strategy for Retriever-Reader Modelscited by 6
  32. 2025Multi-Perspective Data Augmentation for Few-shot Object Detectioncited by 6
  33. 2025ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detectioncited by 6
  34. 2021Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Networkcited by 5
  35. 2021A Competitive Method to VIPriors Object Detection Challengecited by 5
  36. 2021PAFNet: An Efficient Anchor-Free Object Detector Guidancecited by 5
  37. 2021Evolving Image Compositions for Feature Representation Learningcited by 5
  38. 2021Data Augmentation for Scene Text Recognitioncited by 5

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