增强风格的变压器用于建筑场景中的图像标题
Kani Song1, Linlin Chen1, Hengyou Wang1
1School of Science, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.
Entropy (Basel, Switzerland)
|March 28, 2024
概括
一个新的风格增强的图像标题转换器 (SETCAP) 改善了建筑工地监控. 这种模型增强了图像的理解,并为复杂的场景生成了更准确的描述.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 建设管理建设管理.
背景情况:
- 图像标题对于建筑项目情报和现场活动监测至关重要.
- 现有的图像标题模型与建筑场景的复杂性作斗争.
- 缺乏专门的数据集和建筑图像模型阻碍了进步.
研究的目的:
- 开发一个针对复杂建筑环境而定制的高级图像标题模型.
- 提高建筑工地活动生成的标题的准确性和相关性.
- 增强建筑项目管理中的智能和自动化能力.
主要方法:
- 为图像标题 (SETCAP) 提出了一种新型风格增强的变压器.
- 斯温变压器用于网格特征提取,通过样式编码器将样式信息纳入.
- 风格信息被整合到解码器中的文本特征中,用于逐字生成句子.
- 添加了一个句子风格丢失,以使生成的标题与训练数据风格保持一致.
主要成果:
- 拟议的SETCAP模型在MSCOCO和MOCS数据集上取得了显著的改进.
- SETCAP的表现超过了最先进的方法,在MOCS上显示了4.2%的CIDEr得分增加.
- 在MSCOCO数据集上观察到3.9%的CIDEr得分改善,证明了广泛的适用性.
结论:
- 在复杂的建筑场景中,SETCAP模型有效地解决了现有方法的局限性.
- 整合风格信息可以提高专门领域的图像标题的性能.
- 开发的模型为智能施工管理和现场活动分析提供了一个有前途的解决方案.
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