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.

该论文提出了使用张量分解（比如 CPD-EPC 算法）对行人属性识别模型（ALM）进行高效的层次压缩，在资源受限的嵌入式设备上实现高效的轻量级行人属性定位模型（LWALM）的目标。