深度学习可以检测多层次社会灵长类动物中微妙的面部表情
Gu Fang1, Xianlin Peng2,3, Penglin Xie4
1Shaanxi Key Laboratory for Animal Conservation, College of Life Sciences, Northwest University, Xi'an, China.
Integrative zoology
|October 1, 2024
概括
人工智能检测到金色鼻的微妙面部表情,人类看不见. 这一发现增强了我们对灵长类动物沟通和社会结构的理解.
科学领域:
- 灵长类动物的种族学
- 人工智能在生物学中的应用
- 动物沟通动物沟通
背景情况:
- 非人类灵长类动物的面部表情是复杂的,涉及心理,情感和生理因素.
- 微妙的灵长类面部表情的功能意义仍然不确定.
- 金鼻 (Rhinopithecus roxellana) 在复杂的多层次社会中生活.
研究的目的:
- 为了调查黄金鼻的微妙面部表情的存在和检测能力.
- 为了比较人工智能 (AI) 模型与人类观察者在识别这些表达式方面的准确性.
- 探索微妙面部表情在灵长类社会系统中的作用.
主要方法:
- 从视频片段收集了3427张金色鼻的正面图像.
- 利用了三种深度学习模型 (EfficientNet,RepMLP,Tokens-To-Token ViT) 来实现人工智能驱动的面部表情识别.
- 将AI准确度与人类观察者的准确度进行比较 (缺乏经验和经验丰富的组).
主要成果:
- 人类观察者检测到面部表情的平均准确率为32.1% (没有经验) 和45.0% (有经验),接近机会水平 (33%).
- 人工智能深度学习模型实现了显著更高的准确率.
- 性能最好的AI模型在检测微妙的面部表情方面达到94.5%的准确性.
结论:
- 黄金鼻的子表现出微妙的面部表情,人类观察者无法检测到.
- 人工智能是发现动物复杂通信信号的强大工具.
- 微妙的面部表情可能在复杂的灵长类社会系统的进化中起着至关重要的作用.
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