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多模式原型网络用于可解释的情绪分类
Chenguang Song1, Ke Chao2, Bingjing Jia2
1Anhui Science and Technology University, Bengbu, 233000, China. songcg@ahstu.edu.cn.
Scientific reports
|October 27, 2025
概括
本研究介绍了用于多式联网情绪分析的多式联网原型网络 (MMPNet). MMPNet通过识别时间段和模式特征的贡献来提高模型解释性,提高视频数据集的准确性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 情绪分析越来越多地使用多模式视频数据 (视觉,声学,文本).
- 在多式联络情绪分析中,对时段对模型决策的贡献的理解有限.
- 现有的可解释方法在视频中与多式联络和时间依赖性作斗争.
研究的目的:
- 将基于原型的可解释性扩展到多式联络情绪分类.
- 开发一种识别时间段贡献和模式级特征重要性的方法.
- 为了提高多式联络情绪分析模型的可解释性.
主要方法:
- 拟议的多式联络原型网络 (MMPNet) 用于多式联络情绪分类.
- 扩展了基于原型的可解释性,以处理多模式视频数据.
- 开发了用于识别时间级特征贡献和模式级重要性的技术.
主要成果:
- MMPNet实现了卓越的性能,在CMU-MOSI上表现2.9%,在CMU-MOSEI上表现1.6%,优于现有方法.
- 在多式联络情绪分类任务中表现出更高的准确性.
- 为模型预测提供了增强的解释性.
结论:
- MMPNet为可解释的多式联络情绪分析提供了一种新的方法.
- 该方法通过分析时间和模式特征来有效解释预测.
- MMPNet为基于视频的情绪分析的准确性和可解释性设定了新的基准.
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