多视角交叉模态对象编码用于引用表达式理解
概括
本研究引入了新的模块,通过更好地将文本描述与图像对象连接起来,提高引用表达式理解 (REC). 新方法通过考虑上下文关系来增强对象识别,从而在REC任务中获得更准确的结果.
科学领域:
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 引用表达式理解 (REC) 对于使用文本识别图像中的对象至关重要. 现有的两阶段REC方法往往忽略了对象上下文,限制了表示的准确性.
- 目前的方法依赖于预先训练的探测器,忽视了对象及其周围环境之间的关键上下文和关系信息.
研究的目的:
- 通过引入新型模块来增强双阶段引用表达式理解 (REC) 方法.
- 通过结合上下文和关系信息来提高对象表示的区分能力.
主要方法:
- 提出了两个插入和适应模块:表达式引导标签表示 (ELR) 和跨模态校准语义 (CCS) 模块.
- ELR模块将文本表达式名词短语与图像对象标签对齐.
- CCS模块从多个角度将对象特征与邻近的对象特征集成,保存内在信息并结合关系线索.
主要成果:
- 拟议的模块显著加强了图像和文本表示之间的对齐.
- 六个数据集的实验证明了结合先前的统计知识的有效性.
- 该方法实现了竞争性性能,并且与现有的两阶段REC方法兼容.
结论:
- 新的ELR和CCS模块通过捕获关键的上下文和关系信息,有效地增强了两阶段REC方法.
- 整合先前的统计知识和跨模式校准,可以实现更精确的对象嵌入,并提高REC性能.
- 拟议的方法为现有的REC框架提供了多功能增强.
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