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高级多样性特征学习用于行人属性识别

Junyi Wu¹, Yan Huang², Min Gao³

¹Key Laboratory of Network Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University, Fuzhou, Fujian, China.

Neural networks : the official journal of the International Neural Network Society

|April 20, 2025

概括

这项研究引入了一种新的高阶多样性特征学习 (HDFL) 方法,用于行人属性识别 (PAR). 该方法增强了细粒度特征提取,并整合了全球背景,在基准数据集上取得了最先进的结果.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
人工智能的人工智能

背景情况:

步行者属性识别 (PAR) 对于理解城市环境至关重要.
现有的基于部分和基于注意力的 PAR 方法在细节捕捉和全球上下文集成方面存在局限性.
当前的方法往往难以处理细微的细节,并且可能会受到不相关的信息的影响.

研究的目的:

开发一种新的行人属性识别 (PAR) 方法,克服现有方法的局限性.
通过整合高阶统计和全球背景来增强细粒度,属性特定特征的提取.
为了提高PAR系统的准确性和稳定性.

主要方法:

为 PAR 提出了一种高阶多样性特征学习 (HDFL) 方法,利用视觉转换器 (ViT).
引入了特征特定的详细特征探索 (ADFE) 模块,使用多项式预测器捕获高阶统计数据和细粒度特征.
开发了一种软冗余感知损失 (SPLoss),通过测量特征冗余来促进特征多样性.

主要成果:

拟议的HDFL方法在多个PAR数据集上实现了最先进的 (SOTA) 性能.
在具有挑战性的PA100K数据集上表现比以前的SOTA优于1.69%,达到84.92%的平均精度 (mA).
证明了ADFE模块在生成详细,属性特定特征方面的有效性.

结论:

HDFL方法在行人属性识别方面取得了显著的进步.
将详细关注与全球背景相结合,有效地解决了先前的 PAR 技术的局限性.
拟议的方法为复杂的PAR任务提供了强大而准确的解决方案.

关键词:

高层次的多样性是学习特征.步行者属性识别步行者属性识别软冗余感知损失的感知损失

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