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DGPrompt:用于视觉语言模型的双导向提示生成.

Tai Zheng1, Zhen-Duo Chen1, Zi-Chao Zhang1

  • 1School of Software, Shandong University, 1500 Shunhua Road, Jinan 250101, China.

Neural networks : the official journal of the International Neural Network Society
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双导向提示生成 (DGPrompt) 通过改善跨模式对齐和保留一般知识来增强CLIP. 这种方法可以提高视觉识别任务的性能,特别是在有限的数据的情况下.

关键词:
一般化能力的能力.调整方式的调整.快速调整 - 快速调整视觉语言模型的模型.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 自然语言处理自然语言处理.

背景情况:

  • 通过可学习提示,CLIP模型通过可学习提示实现强的性能.
  • 现有的方法受到有限的模式间相互作用和层次上下文的影响,阻碍了对齐.
  • CLIP对提示的敏感性导致了对看不见的类的过拟合和减少泛化.

研究的目的:

  • 提出双导向提示生成 (DGPrompt) 以改善视觉-文本对齐.
  • 通过防止遗忘CLIP的一般知识来提高概括能力.
  • 为了解决现有的CLIP快速调整方法的局限性.

主要方法:

  • DGPrompt利用视觉和文本提示之间的相互指导进行嵌入提取.
  • 整合了一个保留模块,以限制快速调整和保存一般知识.
  • 该方法侧重于有效地对齐视觉和文本表示空间.

主要成果:

  • 与基线CLIP和最先进的MaPLe相比,DGPrompt显示出更高的性能.
  • 在11个数据集的总和平均值上获得了7.84%和0.99%的绝对收益.
  • 在基础到新类的概括和少量学习场景中表现出有效性.

结论:

  • DGPrompt成功地促进了视觉和文本空间之间的对齐.
  • 该方法有效地保留了一般知识,改善了对未见类的概括.
  • DGPrompt为视觉语言模型的基于提示的学习提供了显著的进步.