Person Re-identification (re-id) faces two major challenges: the lack of
cross-view paired training data and learning discriminative identity-sensitive
and view-invariant features in the presence of large pose variations. In this
work, we address both problems by proposing a novel deep person image
generation model for synthesizing realistic person images conditional on the
pose. The model is based on a generative adversarial network (GAN) designed
specifically for pose normalization in re-id, thus termed pose-normalization
GAN (PN-GAN). With the synthesized images, we can learn a new type of deep
re-id feature free of the influence of pose variations. We show that this
feature is strong on its own and complementary to features learned with the
original images. Importantly, under the transfer learning setting, we show that
our model generalizes well to any new re-id dataset without the need for
collecting any training data for model fine-tuning. The model thus has the
potential to make re-id model truly scalable.

本文提出了一种新颖的深度人图像生成模型，利用生成对抗网络，实现了消除视角变化的标准化，生成与姿势相关的真实人物图像，得到新型的去除姿势影响的深度重新识别（re-id）特征，并证明这种特征可以广泛应用于许多不同的 re-id 数据集中。

姿态归一化的人员再识别图像生成

Pose-Normalized Image Generation for Person Re-identification

We propose an architecture for fine-grained visual categorization that
approaches expert human performance in the classification of bird species. Our
architecture first computes an estimate of the object's pose; this is used to
compute local image features which are, in turn, used for classification. The
features are computed by applying deep convolutional nets to image patches that
are located and normalized by the pose. We perform an empirical study of a
number of pose normalization schemes, including an investigation of higher
order geometric warping functions. We propose a novel graph-based clustering
algorithm for learning a compact pose normalization space. We perform a
detailed investigation of state-of-the-art deep convolutional feature
implementations and fine-tuning feature learning for fine-grained
classification. We observe that a model that integrates lower-level feature
layers with pose-normalized extraction routines and higher-level feature layers
with unaligned image features works best. Our experiments advance
state-of-the-art performance on bird species recognition, with a large
improvement of correct classification rates over previous methods (75% vs.
55-65%).

本研究提出一种基于深度卷积网络的姿态标准化方法和特征学习策略，相比于以往算法，在鸟种识别领域取得了更好的分类效果。

使用姿态规范化深度卷积神经网络进行鸟类物种分类

Bird Species Categorization Using Pose Normalized Deep Convolutional  Nets

We propose a method for inferring human attributes (such as gender, hair
style, clothes style, expression, action) from images of people under large
variation of viewpoint, pose, appearance, articulation and occlusion.
Convolutional Neural Nets (CNN) have been shown to perform very well on large
scale object recognition problems. In the context of attribute classification,
however, the signal is often subtle and it may cover only a small part of the
image, while the image is dominated by the effects of pose and viewpoint.
Discounting for pose variation would require training on very large labeled
datasets which are not presently available. Part-based models, such as poselets
and DPM have been shown to perform well for this problem but they are limited
by shallow low-level features. We propose a new method which combines
part-based models and deep learning by training pose-normalized CNNs. We show
substantial improvement vs. state-of-the-art methods on challenging attribute
classification tasks in unconstrained settings. Experiments confirm that our
method outperforms both the best part-based methods on this problem and
conventional CNNs trained on the full bounding box of the person.

本文提出了一种结合基于部分模型和深度学习的姿势归一化卷积神经网络方法，以有效地从人物图片中推断人的属性，包括性别、发型、衣着风格、表情和动作，并且在限制条件下，该方法比目前最佳的基于部分模型和全身边框的 CNN 训练方法表现更好。