基于交换多模式情绪分析的适应式多模式变压器.
Gulanbaier Tuerhong1, Feifan Fu2, Mairidan Wushouer1
1School of Computer Science, Guangdong University of Science and Technology, Guangdong, 523083, China.
Scientific reports
|July 27, 2025
概括
本研究介绍了基于交换 (AMTE) 模型的自适应多式变压器,用于改进情绪分析. AMTE有效地融合了跨模式的情绪线索,提高了对基准数据集的分类准确性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算机视觉 计算机视觉
背景情况:
- 多模态情绪分析利用跨模态情绪线索进行增强的分类.
- 现有的方法与模式分布差异,低效的跨模式交互和上下文相关模型作斗争.
研究的目的:
- 提出基于交换 (AMTE) 模型的自适应多式联络变压器,以应对多式联络情绪分析中的挑战.
- 通过有效地融合跨模态情绪线索来提高情感分类性能.
主要方法:
- 拟议的AMTE模型使用交换融合机制来增强本地特征与其他模式的全球特征.
- 一个多层次的层次融合机制创建了一个自适应的超模态表示,减少模态间分布差异.
- 语言模式作为与超模态表示和上下文信息深度融合的主导模式.
主要成果:
- AMTE实现了高二元情绪分类准确率:在CMU-MOSI上达到了89.18%,在CMU-MOSEI上达到了88.28%,在CH-SIMS上达到了81.84%.
- 该模型有效地弥合了跨模式的语义差异,同时保持了模式特异性.
- 高效的融合和减少的分布差异有助于提高性能.
结论:
- 在多式联运情绪分析方面,AMTE模型显示了显著的改进.
- 拟议的交易所融合和层次融合机制有效地应对跨模式挑战.
- 在多式联运数据中,AMTE为准确的情绪分类提供了一个有希望的方法.
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