  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
