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AIRHF-Net:一个适应性交互表示层次融合网络,用于被封闭的人重新识别和重新识别

Shuze Geng¹, Qiudong Yu², Haowei Wang³

¹School of Information Technology and Engineering, Tianjin University of Technology and Education, Tianjin, 300350, China.

Scientific reports

|November 8, 2024

概括

这项研究介绍了AIRHF-Net,这是一个用于封闭人重新识别的新型网络. 它通过适应性地关注可见区域和增强特征表示来改善行人识别,实现最先进的结果.

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:

视觉转换器 (ViT) 中的稀缺注意力机制非常受欢迎,用于重新识别,但与封闭的行人,长序列和信息丢失作斗争.
由于遗漏关键信息和代币表示趋同问题,现有的方法面临性能恶化.

研究的目的:

引入AIRHF-Net (自适应交互表示层次融合网络) 以在封闭的场景中增强行人重新识别.
克服稀疏注意力机制的局限性,提高特征的可区分性和防堵强度.

主要方法:

开发了一个自适应本地窗口交互编码器 (AL-WIE),结合了窗口,自适应本地和交互注意力,以集中在可见区域的特征提取.
实施了局部层次编码器 (LHE) 来重建空间信息,并增强在更深层网络层中的特征可区分性.
使用了封闭数据增强策略 (ODAS) 来提高模型在封闭下提取关键信息的能力.

主要成果:

在Occluded-DukeMTMC数据集上实现了69.6%的排名-1准确率.
在Occluded-DukeMTMC数据集上获得了61.6%的mAP.
在封闭的行人重新识别任务中表现得更好.

结论:

AIRHF-Net有效地解决了被封闭的人重新识别中的资源消耗和绩效退化问题.
拟议的AL-WIE和LHE组件显著增强特征提取和可区分性,优于现有方法.
整合ODAS进一步加强了该模型在具有挑战性的封闭条件下的稳定性.

关键词:

适应性互动是一种适应性互动.层次化的融合融合.封闭的封闭的重新识别重新识别

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