RandSaliencyAug: Balancing Saliency-Based Data Augmentation for Enhanced Generalization

Teerath Kumar, Alessandra Mileo, Malika Bendechache

2025

Abstract

Improving model generalization in computer vision, especially with noisy or incomplete data, remains a significant challenge. One common solution is image augmentation through occlusion techniques like cutout, random erasing, hide-and-seek, and gridmask. These methods encourage models to focus on less critical information, enhancing robustness. However, they often obscure real objects completely, leading to noisy data or loss of important context, which can cause overfitting. To address these issues, we propose a novel augmentation method, RandSaliencyAug (RSA). RSA identifies salient regions in an image and applies one of six new strategies: Row Slice Erasing, Column Slice Erasing, Row-Column Saliency Erasing, Partial Saliency Erasing, Horizontal Half Saliency Erasing, and Vertical Half Saliency Erasing. RSA is available in two versions: Weighted RSA (W-RSA), which selects policies based on performance, and Non-Weighted RSA (N-RSA), which selects randomly. By preserving contextual information while introducing occlusion, RSA improves model generalization. Experiments on Fashion-MNIST, CIFAR10, CIFAR100, and ImageNet show that W-RSA outperforms existing methods.

Paper Citation

in Harvard Style

Kumar T., Mileo A. and Bendechache M. (2025). RandSaliencyAug: Balancing Saliency-Based Data Augmentation for Enhanced Generalization. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 283-290. DOI: 10.5220/0013098700003912

in Bibtex Style

@conference{visapp25,
author={Teerath Kumar and Alessandra Mileo and Malika Bendechache},
title={RandSaliencyAug: Balancing Saliency-Based Data Augmentation for Enhanced Generalization},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={283-290},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013098700003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - RandSaliencyAug: Balancing Saliency-Based Data Augmentation for Enhanced Generalization
SN - 978-989-758-728-3
AU - Kumar T.
AU - Mileo A.
AU - Bendechache M.
PY - 2025
SP - 283
EP - 290
DO - 10.5220/0013098700003912
PB - SciTePress