SITTA: Single Image Texture Translation for Data Augmentation
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SITTA : Single Image Texture Translation for Data Augmentation. / Li, Boyi; Cui, Yin; Lin, Tsung Yi; Belongie, Serge.
Computer Vision – ECCV 2022 Workshops, Proceedings: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part II. ed. / Leonid Karlinsky; Tomer Michaeli; Ko Nishino. Springer, 2023. p. 3-20 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13802 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - SITTA
T2 - 17th European Conference on Computer Vision, ECCV 2022
AU - Li, Boyi
AU - Cui, Yin
AU - Lin, Tsung Yi
AU - Belongie, Serge
N1 - Funding Information: Acknowledgement. This work was supported in part by the Pioneer Centre for AI, DNRF grant number P1. Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Recent advances in data augmentation enable one to translate images by learning the mapping between a source domain and a target domain. Existing methods tend to learn the distributions by training a model on a variety of datasets, with results evaluated largely in a subjective manner. Relatively few works in this area, however, study the potential use of image synthesis methods for recognition tasks. In this paper, we propose and explore the problem of image translation for data augmentation. We first propose a lightweight yet efficient model for translating texture to augment images based on a single input of source texture, allowing for fast training and testing, referred to as Single Image Texture Translation for data Augmentation (SITTA). Then we explore the use of augmented data in long-tailed and few-shot image classification tasks. We find the proposed augmentation method and workflow is capable of translating the texture of input data into a target domain, leading to consistently improved image recognition performance. Finally, we examine how SITTA and related image translation methods can provide a basis for a data-efficient, “augmentation engineering” approach to model training.
AB - Recent advances in data augmentation enable one to translate images by learning the mapping between a source domain and a target domain. Existing methods tend to learn the distributions by training a model on a variety of datasets, with results evaluated largely in a subjective manner. Relatively few works in this area, however, study the potential use of image synthesis methods for recognition tasks. In this paper, we propose and explore the problem of image translation for data augmentation. We first propose a lightweight yet efficient model for translating texture to augment images based on a single input of source texture, allowing for fast training and testing, referred to as Single Image Texture Translation for data Augmentation (SITTA). Then we explore the use of augmented data in long-tailed and few-shot image classification tasks. We find the proposed augmentation method and workflow is capable of translating the texture of input data into a target domain, leading to consistently improved image recognition performance. Finally, we examine how SITTA and related image translation methods can provide a basis for a data-efficient, “augmentation engineering” approach to model training.
U2 - 10.1007/978-3-031-25063-7_1
DO - 10.1007/978-3-031-25063-7_1
M3 - Article in proceedings
AN - SCOPUS:85151063823
SN - 9783031250620
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 20
BT - Computer Vision – ECCV 2022 Workshops, Proceedings
A2 - Karlinsky, Leonid
A2 - Michaeli, Tomer
A2 - Nishino, Ko
PB - Springer
Y2 - 23 October 2022 through 27 October 2022
ER -
ID: 389598422