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有效的知识蒸和调整,以改善KB-VQA.

Xiaofei Qin1, Ruiqi Pei1, Changxiang He2

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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:

  • 基于知识的视觉问题解答 (KB-VQA) 依赖于外部知识来解决与图像相关的问题.
  • 当前的方法通常使用大型语言模型 (LLM),但难以将视觉特征与知识保持一致,并且需要大量资源.
  • 现有的方法可能会检索不相关的信息,阻碍准确的VQA模型性能.

研究的目的:

  • 为了提出一个高效的方法,EKDA (高效的知识蒸和调整),为KB-VQA.
  • 解决现有的基于LLM的方法的局限性,包括视觉知识对齐不佳和高计算成本.
  • 通过有效地将视觉信息与相关知识相结合,提高VQA的准确性.

主要方法:

  • 使用LLaMA的知识蒸作为教师模型,以有效提取知识.
  • 使用图形神经网络 (GNN) 将视觉特征与提取的知识对齐.
  • 专注于图像特征和知识之间的直接对齐,而不仅仅是图像文本描述.

主要成果:

  • 在OK-VQA数据集上实现了最先进的准确性.
  • 与基线方法相比,显示出6.63%的显著改善.
  • 与其他基于LLM的VQA方法相比,EKDA需要更少的计算资源,并避免复杂的过程.

结论:

  • 通过改善视觉知识对齐,EKDA为 KB-VQA 提供了高效和有效的解决方案.
  • 拟议的方法可以提高语义理解和模型准确性,而无需高计算开销.
  • 这种方法在利用外部知识进行视觉问题答案方面取得了重大进展.