利用高分辨率特征改进基于深度哈希的图像检索
Leveraging High-Resolution Features for Improved Deep Hashing-based Image Retrieval
AI总结：
针对复杂图像检索任务中传统CNN骨干网络特征提取能力不足的问题，提出基于高分辨率网络（HRNet）的深度哈希方法HHNet，在多个基准数据集上取得更优检索性能。
摘要：
深度哈希技术已成为高效图像检索的主流方法。传统上，这些方法使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，数据集日益增加的复杂性给这些骨干架构带来了挑战，使其难以捕获对有效图像检索至关重要的有意义特征。在本研究中，我们探讨了采用通过最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的方法，利用高分辨率网络（HRNets）作为深度哈希任务的骨干网络，称为高分辨率哈希网络（HHNet）。与现有方法相比，我们的方法在所有测试的基准数据集（包括CIFAR-10、NUS-WIDE、MS COCO和ImageNet）上均展现出更优的性能。这种性能提升在复杂数据集上更为显著，这突显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，并为深度哈希任务提供了最优架构的见解。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
