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。

人物再识别的人类语义分析

Human Semantic Parsing for Person Re-identification

The success of deep convolutional architectures is often attributed in part
to their ability to learn multiscale and invariant representations of natural
signals. However, a precise study of these properties and how they affect
learning guarantees is still missing. In this paper, we consider deep
convolutional representations of signals; we study their invariance to
translations and to more general groups of transformations, their stability to
the action of diffeomorphisms, and their ability to preserve signal
information. This analysis is carried by introducing a multilayer kernel based
on convolutional kernel networks and by studying the geometry induced by the
kernel mapping. We then characterize the corresponding reproducing kernel
Hilbert space (RKHS), showing that it contains a large class of convolutional
neural networks with homogeneous activation functions. This analysis allows us
to separate data representation from learning, and to provide a canonical
measure of model complexity, the RKHS norm, which controls both stability and
generalization of any learned model. In addition to models in the constructed
RKHS, our stability analysis also applies to convolutional networks with
generic activations such as rectified linear units, and we discuss its
relationship with recent generalization bounds based on spectral norms.

本文研究了深度卷积体系结构的多尺度不变表示问题，提出了基于卷积核网络的多层核方法，分析了核映射引起的几何学效应，表明可以将数据表示与学习分离，提出了模型复杂度的规范化测量，即控制所学模型的稳定性和泛化能力的重复核希尔伯特空间规范，证明了已有卷积神经网络能够映射到该空间中。

深度卷积表达的群不变性、形变稳定性和复杂性

Group Invariance, Stability to Deformations, and Complexity of Deep  Convolutional Representations

The aim of this paper is to give an overview of domain adaptation and
transfer learning with a specific view on visual applications. After a general
motivation, we first position domain adaptation in the larger transfer learning
problem. Second, we try to address and analyze briefly the state-of-the-art
methods for different types of scenarios, first describing the historical
shallow methods, addressing both the homogeneous and the heterogeneous domain
adaptation methods. Third, we discuss the effect of the success of deep
convolutional architectures which led to new type of domain adaptation methods
that integrate the adaptation within the deep architecture. Fourth, we overview
the methods that go beyond image categorization, such as object detection or
image segmentation, video analyses or learning visual attributes. Finally, we
conclude the paper with a section where we relate domain adaptation to other
machine learning solutions.

本文介绍领域自适应和迁移学习的概述，涵盖不同类型的方案和历史浅层方法、深度卷积架构、超出图像分类的领域适应方法，以及将领域适应与其他机器学习解决方案相关联的讨论。