综述数据增强与对抗学习如何提升图像检索的鲁棒性与精度
A Review of Image Retrieval Techniques: Data Augmentation and Adversarial Learning Approaches
- 结合数据增强生成多样样本，模拟真实场景变化
- 用对抗训练提升模型对光照、遮挡等干扰的抵抗力
- 适合关注图像检索鲁棒性与实战性能的研究者
图像检索是计算机视觉中的关键研究方向，广泛应用于在线商品搜索和安防监控系统。近年来，深度学习的进步显著提升了图像检索的准确性和效率。然而，现有方法在处理大规模数据集、跨域检索以及真实场景下的图像扰动（如光照变化、遮挡、视角差异）方面仍面临挑战。数据增强技术通过生成更多样化的训练样本，模拟真实世界的变化，提升模型泛化能力与鲁棒性，减少过拟合。同时，对抗攻击与防御机制在训练中引入扰动，增强模型对潜在攻击的抵抗能力，保障实际应用中的可靠性。本文全面综述了图像检索领域的最新进展，重点分析数据增强与对抗学习在提升检索性能中的作用，并探讨未来发展方向与潜在挑战。
原文摘要 · Abstract (English)
Image retrieval is a crucial research topic in computer vision, with broad application prospects ranging from online product searches to security surveillance systems. In recent years, the accuracy and efficiency of image retrieval have significantly improved due to advancements in deep learning. However, existing methods still face numerous challenges, particularly in handling large-scale datasets, cross-domain retrieval, and image perturbations that can arise from real-world conditions such as variations in lighting, occlusion, and viewpoint. Data augmentation techniques and adversarial learning methods have been widely applied in the field of image retrieval to address these challenges. Data augmentation enhances the model's generalization ability and robustness by generating more diverse training samples, simulating real-world variations, and reducing overfitting. Meanwhile, adversarial attacks and defenses introduce perturbations during training to improve the model's robustness against potential attacks, ensuring reliability in practical applications. This review comprehensively summarizes the latest research advancements in image retrieval, with a particular focus on the roles of data augmentation and adversarial learning techniques in enhancing retrieval performance. Future directions and potential challenges are also discussed.
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