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This paper introduces a simple extension of mixup (Zhang et al., 2018) data augmentation to enhance generalization in visual recognition tasks. Unlike the vanilla mixup method, which blends entire images, our approach focuses on combining regions from multiple images.
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A Survey on Mixup Augmentations and Beyond
AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification
SUMix: Mixup with Semantic and Uncertain Information
KeepOriginalAugment: Single Image-based Better Information-Preserving Data Augmentation Approach
SUMix: Mixup with Semantic and Uncertain Information
Adversarial AutoMixup
@article{saha2024region,
title = {Region Mixup},
author = {Saptarshi Saha and Utpal Garain},
year = {2024},
eprint = {2409.15028},
archivePrefix = {arXiv},
url = {http://arxiv.org/abs/2409.15028v1}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/2409.15028)