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一个基于跨模式注意力融合和生成数据增强的博物馆文物分类模型
1School of Culture and Museology, Sichuan Vocational College of Cultural Industries, Chengdu, 610213, China.
Scientific reports
|December 18, 2025
概括
这项研究引入了一种新的博物馆文物分类模型,使用交叉模式的注意力和生成数据增强来提高准确性和效率. VBG模型通过解决数据稀缺性和多式联运挑战来增强文化遗产的保护.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数字人文学科 数字人文学科
背景情况:
- 博物馆文物分类对于保护文化遗产至关重要,但受到有限的多式联络数据和注释稀缺性的阻碍.
- 传统和单模深度学习模型缺乏复杂的文物分类任务所需的效率和准确性.
- 整合多样化的数据源和增强数据集是开发强大的分类系统的关键挑战.
研究的目的:
- 提出一种新的博物馆文物分类模型 (VBG模型),克服现有方法的局限性.
- 通过利用多式联运信息和生成数据增强,提高分类准确性和效率.
- 为数字文物管理和更广泛的文化遗产保护领域提供技术解决方案.
主要方法:
- 通过将视觉特征的视觉转换器 (ViT) 和文本语义的BERT集成,开发了一个多式模式框架.
- 实现了双向交互式注意力融合层,以实现模式之间的精确特征对齐.
- 利用生成对抗网络 (GAN) 来增强数据,创建一个"生成反优化"循环来解决数据稀缺问题.
主要成果:
- 在MET和MS COCO数据集上,VBG模型实现了高性能,分类准确率分别为92%和90%.
- 获得了具有竞争力的mAP (0.85和0.83) 和F1得分 (88%和86%),表现优于ResNet和DenseNet等既有模型.
- 废弃性研究证实了跨模式融合和生成数据增强的关键贡献,在它们被删除后,准确性下降了5%-9%.
结论:
- 拟议的VBG模型有效地解决了博物馆文物分类中的多式联运数据挑战和数据稀缺问题.
- 跨模式的注意力融合和生成数据增强是实现高性能的重要组成部分.
- 未来的工作将专注于优化培训时间和生成的图像质量,以提高文物区分和数字保存.
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