Abstract
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
Abstract (translated)
深度哈希技术已成为实现高效图像检索的主要方法。传统方法利用预训练的卷积神经网络（CNN）如AlexNet和VGG-16作为特征提取器。然而，数据集的复杂性对这些骨干架构捕捉有意义特征以实现有效的图像检索造成了挑战。在这项研究中，我们探讨了通过最先进的技术学习高分辨率特征在图像检索任务中的有效性。具体来说，我们提出了一个名为High-Resolution Hashing Network（HHNet）的新方法，作为深度哈希任务的骨架。我们的方法在所有测试基准数据集上的表现都优于现有方法，包括CIFAR-10、NUS-WIDE、MS COCO和ImageNet。这种性能提升在复杂数据集上更加明显，进一步强调了在复杂图像检索任务中学习高分辨率特征的必要性。此外，我们对不同的HRNet配置进行了全面分析，并提供了关于深度哈希任务的最佳架构的洞察。
URL
https://arxiv.org/abs/2403.13747
