Citations

Albumentations is widely used in research areas related to computer vision and deep learning. If you find this library useful for your research, please consider citing:

@article{2018arXiv180906839B,
  author = {A. Buslaev, A. Parinov, E. Khvedchenya, V.~I. Iglovikov and A.~A. Kalinin},
  title = "{Albumentations: fast and flexible image augmentations}",
  journal = {ArXiv e-prints},
  eprint = {1809.06839},
  year = 2018
}

List of papers that cite Albumentations

1. Camera Model Identification Using Convolutional Neural Networks.

October 2018 - A Kuzin, A Fattakhov, I Kibardin.

2. Automatic lesion boundary detection in dermoscopy.

December 2018 - G Kechyn.

3. Automatic salt deposits segmentation: A deep learning approach.

December 2018 - M Karchevskiy, I Ashrapov, L Kozinkin.

4. Adapting Convolutional Neural Networks for Geographical Domain Shift.

January 2019 - P Ostyakov, SI Nikolenko.

5. Cell Nuclear Morphology Analysis Using 3D Shape Modeling, Machine Learning and Visual Analytics.

February 2019 - A Kalinin.

6. Safe Augmentation: Learning Task-Specific Transformations from Data.

February 2019 - I Baran, O Kupyn, A Kravchenko.

7. Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy.

February 2019 - X Liu, A Sinha, M Ishii, GD Hager, A Reiter.

8. U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument.

February 2019 - SM Hasan, CA Linte.

9. General Purpose (GenP) Bioimage Ensemble of Handcrafted and Learned Features with Data Augmentation.

March 2019 - L Nanni, S Brahnam, S Ghidoni, G Maguolo.

10. Breast Tumor Cellularity Assessment using Deep Neural Networks.

May 2019 - A Rakhlin, AA Shvets, AA Kalinin, A Tiulpin.

11. A Deep 3D Object Pose Estimation Framework for Robots with RGB-D Sensors.

June 2019 - AY Wagh.

12. Data Augmentation From RGB to Chlorophyll Fluorescence Imaging Application to Leaf Segmentation of Arabidopsis thaliana From Top View Images.

June 2019 - N Sapoukhina, S Samiei, P Rasti.

13. CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks.

June 2019 - Á Casado-García, C Domínguez.

14. Urban Sound Tagging using Convolutional Neural Networks.

July 2019 - S Adapa.

15. A Framework for Knowing Who is Doing What in Aerial Surveillance Videos.

July 2019 - F Yang, S Sakti, Y Wu, S Nakamura.

16. Visual Anomaly Detection For Automatic Quality Control.

July 2019 - F Piccoli.

17. Physical Cue based Depth-Sensing by Color Coding with Deaberration Network.

August 2019 - N Mishima, T Kozakaya, A Moriya, R Okada.

18. Bayesian Feature Pyramid Networks for Automatic Multi-Label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic Ratio.

August 2019 - R Solovyev, I Melekhov, T Lesonen.

19. DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better.

August 2019 - O Kupyn, T Martyniuk, J Wu, Z Wang.

20. The Impact of Padding on Image Classification by Using Pre-trained Convolutional Neural Networks.

September 2019 - H Tang, A Ortis, S Battiato.

21. A load frame for in situ tomography at PETRA III.

September 2019 - J Moosmann, DCF Wieland.

22. Deep convolutions for in-depth automated rock typing.

September 2019 - EE Baraboshkin, LS Ismailova, DM Orlov.

23. 残差密集相对平均 CGAN 的图像配准方法.

September 2019 - 王丽芳，张程程，秦品乐，蔺素珍，高媛，窦杰亮.

24. Cloud Recognition and Masking of Earth Observation Imagery-An Optimized approach for Automatic Labeling of Sentinel-2 Imagery for Object Detection.

September 2019 - LM Ellefsen.