图像区域的语义增强和对称的语义补充用于文字对图像的人搜索
Ting Tuo1, Lijun Guo2, Rong Zhang1
1Faculty of Electrical Engineering and Computer Science Ningbo, Ningbo University, Ningbo City, 315000, China.
Scientific reports
|July 1, 2025
概括
这项研究介绍了RE-SSC,这是一种用于文本对图像人员搜索 (TIPS) 的新方法. 它增强了图像区域的语义,并使用对称完成来实现更好的交叉模式对齐,提高了搜索准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 面具学习是文字对图像人体搜索 (TIPS) 的一个关键技术.
- 现有的方法在图像区域和文本之间存在语义不一致的问题.
- 当前的方法往往忽视了文本在学习细粒度图像细节中的作用.
研究的目的:
- 为了解决文字对图像人体搜索 (TIPS) 中的语义不一致问题.
- 通过利用文本指导来改善复杂图像细节的学习.
- 为了增强交叉模式对齐,以便更准确地检索行人.
主要方法:
- 图像区域语义增强 (IRSE) 使用超像素细分和自我监督学习来丰富本地图像补丁语义.
- 对称语义完成 (SSC) 采用双重文本和视觉学习过程来实现全球和本地令牌对齐.
- 该RE-SSC方法整合了IRSE和SSC,用于全面的文字到图像的人员搜索 (TIPS).
主要成果:
- 拟议的RE-SSC方法在三个公共数据集上展示了竞争性性能.
- IRSE有效地将高级语义转移到本地图像补丁.
- 通过对称学习,SSC促进了强大的跨模式对齐.
结论:
- 通过增强语义理解和对齐,RE-SSC显著改善了文字对图像的人员搜索 (TIPS).
- 该方法有效地弥合了文本描述和图像区域之间的语义差距.
- RE-SSC为跨模式检索任务的未来研究提供了一个有希望的方向.
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