Pedestrian Attribute Recognition (PAR) deals with the problem of identifying features in a pedestrian image. It has found interesting applications in person retrieval, suspect re-identification and soft biometrics. In the past few years, several Deep Neural Networks (DNNs) have been designed to solve the task; however, the developed DNNs predominantly suffer from over-parameterization and high computational complexity. These problems hinder them from being exploited in resource-constrained embedded devices with limited memory and computational capacity. By reducing a network's layers using effective compression techniques, such as tensor decomposition, neural network compression is an effective method to tackle these problems. We propose novel Lightweight Attribute Localizing Models (LWALM) for Pedestrian Attribute Recognition (PAR). LWALM is a compressed neural network obtained after effective layer-wise compression of the Attribute Localization Model (ALM) using the Canonical Polyadic Decomposition with Error Preserving Correction (CPD-EPC) algorithm.

翻译：行人属性识别（PAR）旨在识别行人图像中的特征，在人 员检索、嫌疑人重识别及软生物特征等领域具有重要应用。近年来，多种深度神经网络（DNN）被设计用于解决该任务，然而现有DNN普遍存在参数冗余及高计算复杂度的问题，阻碍了其在内存和计算能力受限的嵌入式设备中的应用。通过张量分解等高效压缩技术减少网络层数，神经网络压缩是解决这些问题的有效方法。我们提出了针对行人属性识别（PAR）的轻量化属性定位模型（LWALM）。该模型是基于属性定位模型（ALM）采用具有误差保持校正的规范多阶分解（CPD-EPC）算法逐层压缩后得到的压缩神经网络。