通过深度学习和多模式大语言模型理解自然主义面部表情
Yifan Bian1, Dennis Küster2, Hui Liu2
1Department of Experimental Psychology, University College London, London WC1H 0AP, UK.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
本文回顾了用于情感计算的高级面部表情识别 (FER) 工具箱和多模式大语言模型 (MLLMs). 这些系统在现实环境中增强了情感识别,为上下文感知情感模型铺平了道路.
科学领域:
- 情感计算是一种情感计算.
- 人工智能的人工智能是人工智能.
- 心理学 心理学 心理学
背景情况:
- 在自然背景下,面部表情识别 (FER) 研究面临着诸如照明和头部姿势变化等挑战.
- 现有的FER工具箱经常与现实世界的复杂性作斗争,影响识别准确性.
研究的目的:
- 为自然环境中FER提供当前情感计算系统的全面概述.
- 探索多式大型语言模型 (MLLMs) 在推进情感科学和情感识别方面的潜力.
主要方法:
- 审查用户友好的FER工具箱,包括最先进的深度学习模型,包括它们的架构,数据集和性能.
- 讨论多式大型语言模型 (MLLMs) 以及它们在FER和上下文变量量化方面的能力.
主要成果:
- 先进的FER工具箱证明了对现实世界变化的稳定性,提高了识别准确性.
- 通过量化上下文变量,MLLM显示了人类水平的FER能力,并使情感推理能够通过量化上下文变量来实现上下文意识.
结论:
- 目前的FER工具箱在自然环境中提供了更好的性能.
- 通过促进上下文化情感模型的开发,MLLM准备彻底改变情感科学.
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