自律的多晶体交叉模式交互建模用于引用表达式理解
概括
这项研究引入了参考表达式理解 (REC) 的新框架,有效地整合了多层次的视觉和语言信息. 该模型通过从复杂的例子进行自适应式学习来增强语言到视觉本地化.
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:
- 引用表达式理解 (REC) 是一个具有挑战性的视觉语言任务.
- 准确的REC需要整合从视觉和语言模式的多粒度信息.
- 处理多样化的视觉场景和语言变异,特别是在硬实例中,至关重要.
研究的目的:
- 提出一个新的框架,以加强语言到视觉本地化在REC.
- 为了有效地汇总来自不同模式的多层次信息.
- 为了提高模型从丰富信息的艰难例子中学习的能力.
主要方法:
- 提出了一个自律的多粒度交叉模式交互模型框架.
- 设计了一个基于变压器的多粒度交叉模式注意力机制.
- 引入了一种自动步进的样本信息性学习策略,以处理样本差异.
主要成果:
- 拟议的框架显著优于现有的最先进的方法.
- 在广泛使用的数据集上证明有效性:RefCOCO,RefCOCO+,RefCOCOg和RefItGame.
- 网络结构和学习机制的创新有助于提高性能.
结论:
- 自走式多粒度交叉模式交互模型框架有效地解决了REC中的挑战.
- 该方法成功地汇总了多层次的信息,并从艰难的例子中学习.
- 该方法在语言到视觉本地化任务中显示出显著的改进.
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