Abstract
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.
Abstract (translated)
本文介绍了一种新颖的双区域增强方法，旨在减少对大规模标注数据集的依赖，同时提高模型在包括无源领域适应（SFDA）和人员再识别（ReID）等多样化计算机视觉任务中的鲁棒性和适应性。我们的方法通过向前景对象添加随机噪声扰动并空间地打乱背景块来进行有针对性的数据转换。这有效地增加了训练数据的多样性，提高了模型的稳健性和泛化能力。 在PACS数据集上的SFDA评估表明，我们的增强策略始终优于现有的方法，在单目标和多目标适应设置中均实现了显著的准确率提升。通过结构化的变换来扩充训练数据，我们的方法使模型能够跨领域进行推广，并提供了一种可扩展的解决方案以减少对人工标注数据集的依赖。 此外，在Market-1501和DukeMTMC-reID数据集上的实验验证了我们方法在人员ReID任务中的有效性，超过了传统的增强技术。
URL
https://arxiv.org/abs/2504.13077
