Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled Data
This work addresses the challenge of data scarcity in computer vision tasks like domain adaptation and person re-identification, offering a scalable solution to reduce manual annotation needs, though it is incremental as it builds on existing augmentation methods.
The paper tackles the problem of reducing reliance on large labeled datasets in computer vision by introducing a dual-region augmentation method that applies random noise to foreground objects and shuffles background patches, achieving significant accuracy improvements on datasets like PACS for source-free domain adaptation and outperforming traditional techniques on person re-identification datasets such as Market-1501 and DukeMTMC-reID.
This paper introduces a novel dual-region augmentation approach designed to reduce reliance on large-scale labeled datasets while improving model robustness and adaptability across diverse computer vision tasks, including source-free domain adaptation (SFDA) and person re-identification (ReID). Our method performs targeted data transformations by applying random noise perturbations to foreground objects and spatially shuffling background patches. This effectively increases the diversity of the training data, improving model robustness and generalization. Evaluations on the PACS dataset for SFDA demonstrate that our augmentation strategy consistently outperforms existing methods, achieving significant accuracy improvements in both single-target and multi-target adaptation settings. By augmenting training data through structured transformations, our method enables model generalization across domains, providing a scalable solution for reducing reliance on manually annotated datasets. Furthermore, experiments on Market-1501 and DukeMTMC-reID datasets validate the effectiveness of our approach for person ReID, surpassing traditional augmentation techniques. The code is available at https://github.com/PrasannaPulakurthi/Foreground-Background-Augmentation