越多越好:通过跨模式的洞察力从文本中检测出分类情绪
Changrong Min1, Aimin Wang1, Ximing Li2
1Criminal Investigation Police University of China, China.
概括
本研究介绍了一种以视觉情感前为指导的情感探测器 (VisPor),用于分类情感检测 (CED). VisPor利用视觉信息来创建有效的提示,大大提高了情绪检测准确度,相比现有方法.
科学领域:
- 自然语言处理自然语言处理.
- 计算机视觉 计算机视觉
- 情感计算是一种情感计算.
背景情况:
- 分类情绪检测 (CED) 旨在识别文本中的情绪.
- 提示调整 (PT) 用于CED,但设计高效的提示仍然是一个挑战.
- 人类的感知本质上是交叉模式的,这表明整合多种模式的实用性.
研究的目的:
- 提出一种新的方法,视觉情绪前引导情绪探测器 (VisPor),用于增强分类情绪检测.
- 解决CED中现有的快速设计方法的局限性.
- 利用跨模式信息,特别是视觉数据,改进基于文本的情感识别.
主要方法:
- VisPor使用来自情感资源的视觉信息来构建CED的高效任务提示.
- 一个关键的方法是将抽象的文字情感描述与具体的情感图像在共享的嵌入空间中对齐,以弥合模式差距.
- 视觉丰富的文本嵌入被用作前来指导模型捕捉微妙的文本情感特征.
主要成果:
- 在三个已建立的CED基准上进行了广泛的实验.
- 与现有的最先进的方法相比,VisPor显示出显著的性能改善.
- 视觉信息的整合有效地提高了模型在文本中检测情绪的能力.
结论:
- 拟议的VisPor模型通过有效地整合视觉线索,为分类情绪检测提供了一种优越的方法.
- 通过交叉模式对齐来弥合模式差距对于推进情绪检测任务至关重要.
- 维斯波尔的快速建设战略对于CED来说是非常有效的,其表现优于目前的技术.
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