Deep hashing techniques have emerged as the predominant approach for efficient image retrieval. Traditionally, these methods utilize pre-trained convolutional neural networks (CNNs) such as AlexNet and VGG-16 as feature extractors. However, the increasing complexity of datasets poses challenges for these backbone architectures in capturing meaningful features essential for effective image retrieval. In this study, we explore the efficacy of employing high-resolution features learned through state-of-the-art techniques for image retrieval tasks. Specifically, we propose a novel methodology that utilizes High-Resolution Networks (HRNets) as the backbone for the deep hashing task, termed High-Resolution Hashing Network (HHNet). Our approach demonstrates superior performance compared to existing methods across all tested benchmark datasets, including CIFAR-10, NUS-WIDE, MS COCO, and ImageNet. This performance improvement is more pronounced for complex datasets, which highlights the need to learn high-resolution features for intricate image retrieval tasks. Furthermore, we conduct a comprehensive analysis of different HRNet configurations and provide insights into the optimal architecture for the deep hashing task

翻译：深度哈希技术已成为高效图像检索的主流方法。传统上，这些方法利用预训练的卷积神经网络（如AlexNet和VGG-16）作为特征提取器。然而，数据集的日益复杂性对这些主干架构在捕获有效图像检索所需的有意义特征方面提出了挑战。本研究探讨了采用通过先进技术学习到的高分辨率特征在图像检索任务中的有效性。具体而言，我们提出了一种新颖方法，使用高分辨率网络（HRNets）作为深度哈希任务的主干，称之为高分辨率哈希网络（HHNet）。我们的方法在所有测试基准数据集（包括CIFAR-10、NUS-WIDE、MS COCO和ImageNet）上均展现出优于现有方法的性能。这种性能提升在复杂数据集中更为显著，凸显了为复杂图像检索任务学习高分辨率特征的必要性。此外，我们全面分析了不同HRNet配置，并针对深度哈希任务提供了最优架构的见解。