This research focuses on the exploration of the most widely adopted libraries specifically designed for data augmentation in computer vision tasks. Here we aim at providing a comprehensive survey of publicly available data augmentation libraries, facilitating practitioners in navigating through these resources effectively. Through a curated taxonomy, we present an organized classification of different approaches employed by these libraries, along with accompanying application examples. By examining the techniques of each library, practitioners can make informed decisions in selecting the most suitable augmentation techniques for their computer vision projects. To ensure the accessibility of this valuable information, a dedicated public website named DALib has been created. This website serves as a centralized repository where the taxonomy, methods, and examples associated with the surveyed data augmentation libraries can be explored. By offering this comprehensive resource, we aim to empower practitioners and contribute to the advancement of computer vision research and applications through effective utilization of data augmentation techniques.
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Publications
1.
DALib: A Curated Repository of Libraries for Data Augmentation in Computer Vision
(Sofia Amarù, Davide Marelli, Gianluigi Ciocca, Raimondo Schettini)
In Journal of Imaging, volume 9, number 10, 2023.
BibTex
Doi
Project Page
@article{amaru2023dalib,
author = {Amarù, Sofia and Marelli, Davide and Ciocca, Gianluigi and Schettini, Raimondo},
year = {2023},
title = {DALib: A Curated Repository of Libraries for Data Augmentation in Computer Vision},
volume = {9},
number = {10},
journal = {Journal of Imaging},
url = {https://www.mdpi.com/2313-433X/9/10/232},
doi = {10.3390/jimaging9100232},
projectref = {http://www.ivl.disco.unimib.it/activities/dalib/}}