Existing alignment-based methods have to employ the pretrained human parsing
models to achieve the pixel-level alignment, and cannot identify the personal
belongings (e.g., backpacks and reticule) which are crucial to person re-ID. In
this paper, we propose the identity-guided human semantic parsing approach
(ISP) to locate both the human body parts and personal belongings at
pixel-level for aligned person re-ID only with person identity labels. We
design the cascaded clustering on feature maps to generate the pseudo-labels of
human parts. Specifically, for the pixels of all images of a person, we first
group them to foreground or background and then group the foreground pixels to
human parts. The cluster assignments are subsequently used as pseudo-labels of
human parts to supervise the part estimation and ISP iteratively learns the
feature maps and groups them. Finally, local features of both human body parts
and personal belongings are obtained according to the selflearned part
estimation, and only features of visible parts are utilized for the retrieval.
Extensive experiments on three widely used datasets validate the superiority of
ISP over lots of state-of-the-art methods. Our code is available at
this https URL

本文提出了一种基于身份指导的人类语义解析方法（ISP），通过级联聚类来生成伪标签，并仅利用人的身份标签来定位人体部位和个人物品并实现像素级别的对齐，最终从中获得人体部位和个人物品的本地特征，验证表明，该方法在三个被广泛使用的数据集上优于大量先进方法。

身份引导的人体语义分割用于人员再识别

Identity-Guided Human Semantic Parsing for Person Re-Identification

Person re-identification is a challenging task mainly due to factors such as
background clutter, pose, illumination and camera point of view variations.
These elements hinder the process of extracting robust and discriminative
representations, hence preventing different identities from being successfully
distinguished. To improve the representation learning, usually, local features
from human body parts are extracted. However, the common practice for such a
process has been based on bounding box part detection. In this paper, we
propose to adopt human semantic parsing which, due to its pixel-level accuracy
and capability of modeling arbitrary contours, is naturally a better
alternative. Our proposed SPReID integrates human semantic parsing in person
re-identification and not only considerably outperforms its counter baseline,
but achieves state-of-the-art performance. We also show that by employing a
\textit{simple} yet effective training strategy, standard popular deep
convolutional architectures such as Inception-V3 and ResNet-152, with no
modification, while operating solely on full image, can dramatically outperform
current state-of-the-art. Our proposed methods improve state-of-the-art person
re-identification on: Market-1501 by ~17% in mAP and ~6% in rank-1, CUHK03 by
~4% in rank-1 and DukeMTMC-reID by ~24% in mAP and ~10% in rank-1.

本论文旨在提高人物再识别的性能。与常见的检测局部特征的方法不同，我们提出采用人类语义分析来提取局部特征，相应的性能得到了大幅提升，并成为了当前最优方法。此外，我们展示了通过简单且有效的训练策略可以使标准的卷积神经网络方法，如 Inception-V3 和 ResNet-152，无需修改即可显著提高人物再识别的性能。