一个以性能为导向的混合文本图像分类模型,用于多式联网数据
1Department of Computer Science and Applications, Rohtak, 124001, India. swati.rs20.dcsa@mdurohtak.ac.in.
Scientific reports
|April 4, 2025
概括
一个新的混合深度学习模型 (HTIC) 集成文本和图像数据,以进行高级分类. 这种多模式方法提高了分类的准确性和通用性,在各种数据集上优于现有的方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 深度学习模型经常与多种类型的数据作斗争,这限制了它们在现实应用中的有效性.
- 涉及文本和视觉信息的分类任务需要复杂的方法来处理各种数据模式.
研究的目的:
- 引入混合文本图像分类 (HTIC) 模型,这是一种用于处理多种类型数据的新型深度学习架构.
- 评估HTIC模型与各种数据集中的其他分类方法的性能.
主要方法:
- 该HTIC模型采用复杂的深度学习架构,集成VGG16用于图像分类,并将Roberta与优化的CNN用于文本分类.
- 使用多模特特征提取层来确保图像和文本表示之间的兼容性.
- 该模型利用罗伯塔从文本嵌入中提取信息并捕获跨模式模式的能力.
主要成果:
- 与其他分类算法相比,HTIC模型在五个不同的数据集中表现出卓越的性能.
- 该模型显示了对输入数据变异的增强概括性和稳定性.
- 该研究强调了混合建模在提高分类准确性和可解释性方面的有效性.
结论:
- HTIC 模型代表了多式联运数据分析的重大进步,提供了更好的分类准确性和可靠性.
- 它的强大性能和通用性使其适用于现实世界的应用,包括对NFT数据集的分析.
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