标题： 不同深度神经网络模型在文化遗产领域的比较 显示英文标题

标题： Comparison of Different Deep Neural Network Models in the Cultural Heritage Domain

摘要： 计算机视觉与深度学习的结合是记录和保护文化遗产以及提升游客体验的重要组成部分。 近年来，在计算机视觉领域建立了两种深度学习范式：卷积神经网络和变换器架构。 本研究旨在对这两种技术的一些代表作进行比较分析，评估它们从通用数据集（例如 ImageNet）迁移到文化遗产特定任务中的知识迁移能力。 测试结果显示，VGG、ResNet、DenseNet、视觉变换器、Swin 变换器和 PoolFormer 架构示例的结果表明，DenseNet 在效率-可计算性比方面表现最佳。 显示英文摘要

摘要： The integration of computer vision and deep learning is an essential part of documenting and preserving cultural heritage, as well as improving visitor experiences. In recent years, two deep learning paradigms have been established in the field of computer vision: convolutional neural networks and transformer architectures. The present study aims to make a comparative analysis of some representatives of these two techniques of their ability to transfer knowledge from generic dataset, such as ImageNet, to cultural heritage specific tasks. The results of testing examples of the architectures VGG, ResNet, DenseNet, Visual Transformer, Swin Transformer, and PoolFormer, showed that DenseNet is the best in terms of efficiency-computability ratio.