重新审视基于文本的个人检索:缓解注释诱导的不匹配与多模式大语言模型
Zihang Han1, Chao Zhu1, Mengyin Liu1
1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
现有的基于文本的个人检索基准包含模两可的描述,导致模型评估不准确. 本研究引入了一个注释改进框架,使用多式联运大型语言模型生成独特的描述,提高基准质量和TBPR模型性能.
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:
- 基于文本的个人检索 (TBPR) 依赖于高质量的基准来准确评估模型.
- 当前的TBPR基准受到模两可的注释的影响,相似的图像具有相同的描述,导致评估错误.
- 在不考虑类似图像的情况下,单独的图像注释会阻碍创建独特的描述.
研究的目的:
- 提出一个注释改进框架,以提高TBPR基准的质量.
- 为了减轻TBPR模型评估中的注释引起的不匹配.
- 提高人形象的文字描述的辨别力.
主要方法:
- 使用TBPR模型,自动识别容易出现不匹配的图像集.
- 利用多式联通大型语言模型 (MLLMs) 同时处理多个图像.
- 为每个图像生成独特的描述,并替换原始注释以提高质量.
主要成果:
- 拟议的框架有效地提高了TBPR基准中的注释质量.
- 在CUHK-PEDES,RSTPReid和ICFG-PEDES上的实验验验证了该方法的有效性.
- 框架产生的更具歧视性的标题有利于主流的TBPR模型.
结论:
- 注释精细化框架显著提高了TBPR基准的质量.
- 改进的基准可以更准确地评估TBPR模型.
- 增强的基准数据集将公开发布,以造福研究界.
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