在艺术品中探索情绪刺激检测:一个基准数据集和基线评估
Tianwei Chen1, Noa Garcia1, Liangzhi Li2
1Intelligence and Sensing Lab, Osaka University, Suita, Osaka 565-0871, Japan.
Journal of imaging
|June 26, 2024
概括
研究人员开发了一项用于检测艺术中的情感刺激的新任务,创建了APOLO数据集,以对AI情感识别进行基准测试. 这项工作突出了艺术分析的挑战和当前AI的局限性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 艺术分析 艺术分析
背景情况:
- 在艺术中理解人类情感是复杂的,因为不同的风格和主观的解释.
- 当前的人工智能模型在艺术品中识别和定位情绪触发器方面面临挑战.
研究的目的:
- 介绍一项用于在艺术品中检测情绪刺激的新任务.
- 建立一个基准来评估AI在艺术中处理人类情感的能力.
- 创建一个数据集来量化情绪刺激检测性能.
主要方法:
- 开发了一个"情绪刺激检测任务",专注于识别在艺术中唤起情绪的区域.
- 通过群众来源,对情绪刺激的像素级注释构建了阿波罗数据集.
- 在数据集上评估了八种基线方法,包括一个专门的模型.
主要成果:
- 阿波罗数据集包括6,781种情绪刺激,涵盖4,718件艺术品.
- 实验揭示了任务的难度和当前人工智能技术在艺术情感分析中的局限性.
- 定性和定量分析都表明了这些挑战.
结论:
- 情绪刺激检测任务和阿波罗数据集为推动人工智能在艺术情感理解方面提供了宝贵的资源.
- 需要进一步的研究来提高人工智能在各种艺术风格中解释主观情感内容的能力.
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