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在基于文本的VQA的图形推理中探索稀疏的空间关系.

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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:

    • 基于文本的视觉问题答案 (TextVQA) 涉及多个对象和光学字符识别 (OCR) 代币之间的复杂关系推断.
    • 现有的方法经常处理所有视觉关系,导致冗余和低效.
    • 识别和使用最相关的关系对于提高TextVQA性能至关重要.

    研究的目的:

    • 在TextVQA.中解决冗余关系推理的挑战.
    • 开发一种新的方法来识别和修剪多余的视觉连接.
    • 提高TextVQA模型的准确性和可解释性.

    主要方法:

    • 提出一个Sparse Spatial Graph Network (SSGN),其中包含一个空间意识的关系修剪技术.
    • 使用空间因素,如距离,几何维度,重叠面积和DIoU进行修剪.
    • 采用渐进式图形学习架构,考虑对象对象,OCR-OCR和对象-OCR令牌关系.

    主要成果:

    • 在TextVQA和ST-VQA数据集上,SSGN表现出有前途的表现.
    • 提出的空间意识的修剪有效地减少了冗余的关系推理.
    • 可视化结果证实了SSGN方法的可解释性.

    结论:

    • SSGN有效地削减了TextVQA中的冗余关系,从而提高了性能.
    • 空间意识关系修剪是增强复杂场景中的视觉推理的可行技术.
    • 通过专注于关键视觉连接,SSGN模型为TextVQA提供了一种可解释的方法.