属性快速对齐网络用于零射击学习
IEEE transactions on neural networks and learning systems
|August 19, 2025
概括
属性提示调整网络 (APAN) 通过使用属性提示调整调整CLIP模型的特征来改进零射击学习 (ZSL). 这增强了知识传输,以获得更好的视觉语义表示.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的零射击学习 (ZSL) 依赖于知识转移的类别属性.
- 像CLIP这样的大型语言模型使用类别名称来进行类似ZSL的预测.
- 现有的方法很难将CLIP的优势整合到传统的ZSL框架中.
研究的目的:
- 改进从预训练的CLIP模型到下游ZSL框架的知识可转移性.
- 通过利用属性信息来开发可概括的特征表示.
- 调查CLIP对ZSL在大型模型时代的影响.
主要方法:
- 属性提示调整 (APT) 来从类描述中生成属性提示.
- 使用双分支APAN架构进行跨网络功能对齐 (CFA).
- 视觉语义交互的注意力,以指导视觉区域在结网络中的本地化.
- 预测对齐损失,以限制来自跨网络视觉特征的预测.
主要成果:
- APAN逐步完善和调整跨网络的功能.
- 该方法捕获细粒度的属性信息,用于可概括的表示.
- 在三个基准数据集上,APAN的性能优于最先进的方法.
- APAN有效地吸收了从CLIP模型中概括的知识.
结论:
- APAN成功地弥合了CLIP和传统ZSL之间的差距.
- 拟议的方法增强了特征表示,以提高ZSL的性能.
- 在ZSL任务中利用大型预训练模型,APAN提供了一种可行的方法.
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