LViT-Net:一个域概括人重新识别模型,结合了局部语义和多功能交叉融合
Xintong Hu1, Peishun Liu2, Xuefang Wang3
1Faculty of Information Science and Engineering, Ocean University of China, Qingdao, Shandong, 266100, China.
Visual computing for industry, biomedicine, and art
|April 16, 2025
概括
通过融合本地和全球特征,LViT-Net提高了对个人重新识别 (ReID) 的域概括性. 这种新的方法提高了跨不同数据集的行人识别,优于现有的方法.
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:
- 人重新识别 (ReID) 面临挑战,因为在行人特征上存在显著的类内变异性和类间相似性.
- 现有的ReID方法难以进行域泛化,无法有效地捕捉全球上下文和本地空间信息.
研究的目的:
- 提出LViT-Net,这是一个创新的方法,用于域名泛化人ReID.
- 增强ReID模型对未见域的稳定性和概括能力.
主要方法:
- 为了提取本地和全球的歧视性特征,LViT-Net使用具有平行等级结构的双分支编码器.
- 一个局部多尺度特征融合模块捕捉了各种尺度的细粒度局部特征.
- 双功能交叉融合模块将本地特征与全球语义信息集成在一起,以实现相互改进.
主要成果:
- LViT-Net有效地平衡了详细和整体的信息,创建了强大的行人特征表示.
- 实验结果表明LViT-Net在单源和多源域泛化ReID任务中优于最先进的方法.
结论:
- 拟议的LViT-Net方法显著改善了个人ReID的域概括.
- 局部语义和多功能交叉融合的融合为具有挑战性的ReID场景提供了强大的解决方案.
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