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TBKIN:基于值的显式选择,用于增强跨模态语义对齐.

Zihan Guo1, Xiang Shen2,3, Chongqing Chen2

  • 1Department of Computer Science, Changzhi University, Changzhi, Shanxi, China.

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此摘要是机器生成的。

本研究引入了一种新的视觉语言模型 (TBKIN),通过减少无关数据干扰来增强语义对齐. TBKIN在VQA 2.0和RefCOCO数据集上取得了最先进的结果,改善了多模式学习.

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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:

  • 视觉语言模型将视觉和语言数据集成为多模式任务.
  • 现有的模型很难将不相关数据的干扰降到最低,从而限制了性能.
  • 图像-文本对之间的有效语义对齐至关重要.

研究的目的:

  • 提出一种新的视觉语言模型,即基于值的知识整合网络 (TBKIN).
  • 为了有效地捕获模式内和模式间的知识,同时减轻外部信息.
  • 增强语义对齐并减少视觉语言任务中的干扰.

主要方法:

  • TBKIN使用统一的场景图形结构和先进的掩饰策略.
  • 基于值选择的微调策略被用来消除噪音.
  • 该模型整合了模式内和模式间的知识.

主要成果:

  • 在VQA 2.0数据集上实现了73.90%的最先进的准确性.
  • 在RefCOCO数据集上实现了84.60%的最新精度.
  • 在四个基准数据集中证明了稳定性和卓越性能.

结论:

  • TBKIN有效地减少干扰,同时在视觉语言任务中增强语义对齐.
  • 该模型显示了促进多模式学习的巨大潜力.
  • 为现实世界应用提供实用和有效的解决方案.