通过情绪刺激意识的标题改进了IEC性能
Zibo Zhou1, Zhengjun Zhai2, Xin Gao1
1School of Computer Science, Northwestern Polytechnical University, No.127 Youyi Xilu, Xi'an, 710072, Shaanxi, China.
Scientific reports
|July 2, 2025
概括
本研究引入了一种新的图像情感分类 (IEC) 方法,使用生成图像字幕来弥合视觉数据和情感理解之间的差距. 这种方法显著提高了从图像中辨别情绪的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 心理学 心理学 心理学
背景情况:
- 图像情感分类 (IEC) 旨在识别图像中的情感.
- 当前的方法与视觉特征和高级情绪之间的情感差距作斗争.
- 现有的技术通常依赖于语义信息,而语义信息具有局限性.
研究的目的:
- 通过将视觉特征与来自图像标题的辅助信息相结合来增强图像情感分类.
- 通过利用自然语言处理的进步来解决IEC的情感差距.
- 开发一个新的网络,用于情感意识的图像标题和分类.
主要方法:
- 介绍了情绪刺激意识的标题网络 (ESCNet),用于增强视觉表示.
- 开发了一套情感标题数据集,用于预训练与情感相关的标题生成.
- 利用融合模块与交叉注意力和自我注意力来关联图像和标题功能.
- 实现了可变减重功能,以专注于具有挑战性的样本.
主要成果:
- 拟议的ESCNet方法在多个公共数据集上表现出优越的性能,与最先进的模型相比.
- 废弃研究和可视化证实了网络组件的有效性.
- 整合生成字幕显著改善了情绪辨别能力.
结论:
- 该研究通过结合视觉和文本信息,成功地弥合了IEC的情感差距.
- ESCNet为更准确和更强大的图像情感分类提供了一个有希望的方向.
- 这些发现突出了利用NLP技术在计算机视觉任务中的潜力.
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