头部内部和头部间的正交注意力用于图像标题化
概括
这项研究引入了正交注意力 (I2OA),以改善图像标题中的多头注意力. 它增强了注意力集中,减少了冗余,从而导致更好的图像描述.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 多头注意力 (MA) 对于图像标题至关重要,使模型能够专注于来自不同表示子空间的关键信息.
- 当前的MA方法缺乏确保在子空间中适当地分配注意力的机制,导致头部过度集中和冗余.
- 这限制了图像标题中的注意力机制的多样性和表现力.
研究的目的:
- 提出一种新的头部内部和头部间正角注意力 (I2OA) 机制,以增强图像标题的MA.
- 在现有的MA模型中解决过度集中注意力和头部冗余的问题.
- 提高图像标题模型的性能,而不会增加复杂性或参数.
主要方法:
- 引入头部内对角注意力通过在每个头部内应用对角约束来使注意力从物体中心分散到内容意识.
- 实现了头间直角注意力,通过在头之间应用直角约束来减少头之间的冗余,增强子空间多样性.
- 将I2OA集成到现有的基于注意力的多头图像标题框架中.
主要成果:
- 拟议的I2OA方法有效地提高了MS COCO数据集上的图像标题模型的性能.
- 头部内直角注意力导致了更全面的内容意识注意力.
- 间头直角注意力成功地减少了头部冗余和增加了表示多样性.
结论:
- I2OA提供了一种高效和灵活的方法来改善图像标题的多头注意力.
- 正交正规化有效地解决了注意力集中和头部冗余性的限制.
- 该方法在没有额外的模型复杂性或参数的情况下证明了显著的性能增长.
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