A Review of Image Retrieval Techniques: Data Augmentation and Adversarial Learning Approaches

TL;DR

This paper reviews recent advancements in image retrieval, emphasizing how data augmentation and adversarial learning improve accuracy, robustness, and generalization in large-scale and real-world scenarios.

Contribution

It provides a comprehensive summary of the latest research on data augmentation and adversarial learning techniques in image retrieval, highlighting their roles and future challenges.

Findings

Data augmentation improves model robustness and generalization.

Adversarial learning enhances model resilience against attacks.

Recent methods significantly boost retrieval accuracy in complex scenarios.

Abstract

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.