Papers › Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled Data
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled Data
Prasanna Reddy Pulakurthi, Majid Rabbani, Celso M. de Melo, Sohail A. Dianat, Raghuveer M. Rao
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.
Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Source-Free Domain Adaptation | PACS | DRA | Average Accuracy | 84.0 | #2 of 3 | Archive leaderboard | report |
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