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马巴ReID:利用Vision Mamba进行多模式对象重新识别

Ruijuan Zhang^1,2, Lizhong Xu¹, Song Yang¹

¹School of Computer and Information, Hohai University, Nanjing 211106, China.

Sensors (Basel, Switzerland)

|July 27, 2024

概括

通过使用VMamba,MambaReID通过融合CNN和变压器的优势来增强多模式对象重新识别 (ReID). 这种新的框架实现了优越的ReID性能,并降低了计算成本和参数.

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:

多模态对象重新识别 (ReID) 利用不同图像模式的互补信息.
传统的卷积神经网络 (CNN) 方法具有有限的受体场.
基于变压器的方法面临着高的计算需求,缺乏卷积偏差.

研究的目的:

提出一种新的融合框架,MambaReID,它集成了CNN和变压器的优势,用于改进多模式对象ReID.
通过结合有效的VMamba架构来克服现有方法的局限性.

主要方法:

开发了MambaReID,这是一个融合框架,包括三级VMamba (TSV),密集的Mamba (DM) 和一致的VMamba融合 (CVF).
TSV以低计算复杂度高效地捕捉全球背景和本地细节.
通过密集整合跨模式信息,DM增强了特征的可区分性.
CVF提供细粒度的模式聚合,以提高功能稳定性.

主要成果:

在多模式对象ReID任务中,MambaReID实现了卓越的性能.
与现有方法相比,该框架显示了降低的参数和较低的计算成本.
通过对三种多模式对象ReID基准进行广泛实验验证的有效性.

结论:

拟议的MambaReID框架有效地解决了CNN和基于变压器的方法在多模式对象ReID中的局限性.
MambaReID为跨模式对象识别提供了一个计算效率高和高性能解决方案.

关键词:

在这里,我们可以看到VMamba VMamba.一致的VMamba融合有密集的连接连接.模式聚合模式聚合多模态对象ReID ReID是一个多模态对象.

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