Summary of Augmentory: a Fast and Flexible Polygon Augmentation Library, by Tanaz Ghahremani et al.
AugmenTory: A Fast and Flexible Polygon Augmentation Library
by Tanaz Ghahremani, Mohammad Hoseyni, Mohammad Javad Ahmadi, Pouria Mehrabi, Amirhossein Nikoofard
First submitted to arxiv on: 7 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel solution to address the challenge of limited datasets in image processing, specifically focusing on instance segmentation using polygons. The proposed approach, embodied in the AugmenTory library, offers reduced computational demands compared to existing methods and includes a postprocessing thresholding feature. The authors thoroughly test data augmentation techniques such as geometric transformations and color space adjustments for their ability to artificially expand training datasets and generate semi-realistic data for training purposes. The paper also highlights the importance of addressing limited datasets in image processing and the potential impact of the proposed approach on advancing instance segmentation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data augmentation is a technique used to make machine learning models more robust by creating artificial versions of real-world images. This helps to solve the problem of having too few training examples, which can limit what an AI model can learn. The paper describes a new library called AugmenTory that makes it easier to create these artificial images. It also talks about how this library is faster and more efficient than other methods. |
Keywords
» Artificial intelligence » Data augmentation » Instance segmentation » Machine learning
