In this paper, we address the problem of makeup transfer, which aims at
transplanting the makeup from the reference face to the source face while
preserving the identity of the source. Existing makeup transfer methods have
made notable progress in generating realistic makeup faces, but do not perform
well in terms of color fidelity and spatial transformation. To tackle these
issues, we propose a novel Facial Attribute Transformer (FAT) and its variant
Spatial FAT for high-quality makeup transfer. Drawing inspirations from the
Transformer in NLP, FAT is able to model the semantic correspondences and
interactions between the source face and reference face, and then precisely
estimate and transfer the facial attributes. To further facilitate shape
deformation and transformation of facial parts, we also integrate thin plate
splines (TPS) into FAT, thus creating Spatial FAT, which is the first method
that can transfer geometric attributes in addition to color and texture.
Extensive qualitative and quantitative experiments demonstrate the
effectiveness and superiority of our proposed FATs in the following aspects:
(1) ensuring high-fidelity color transfer; (2) allowing for geometric
transformation of facial parts; (3) handling facial variations (such as poses
and shadows) and (4) supporting high-resolution face generation.

本文提出了一种基于 Facial Attribute Transformer（FAT）和其空间变体 Spatial FAT 的高质量化妆转移方法，利用 Transformer 模型语义对源面部和参考面部之间的对应关系和相互作用进行建模，进而准确地估计和转移面部属性，同时通过集成 Thin Plate Splines（TPS），Spatial FAT 还可以转移几何属性，而且在颜色保真度和空间变换方面表现优异，经过严格的定性和定量实验验证了所提出方法的有效性和优越性。

面部属性转换器实现精准和强健的化妆迁移

Facial Attribute Transformers for Precise and Robust Makeup Transfer

Learning to insert an object instance into an image in a semantically
coherent manner is a challenging and interesting problem. Solving it requires
(a) determining a location to place an object in the scene and (b) determining
its appearance at the location. Such an object insertion model can potentially
facilitate numerous image editing and scene parsing applications. In this
paper, we propose an end-to-end trainable neural network for the task of
inserting an object instance mask of a specified class into the semantic label
map of an image. Our network consists of two generative modules where one
determines where the inserted object mask should be (i.e., location and scale)
and the other determines what the object mask shape (and pose) should look
like. The two modules are connected together via a spatial transformation
network and jointly trained. We devise a learning procedure that leverage both
supervised and unsupervised data and show our model can insert an object at
diverse locations with various appearances. We conduct extensive experimental
validations with comparisons to strong baselines to verify the effectiveness of
the proposed network.

本文提出了一种基于端到端可训练神经网络的方法，以在图像的语义标签地图中插入特定类别的对象实例。该神经网络由两个生成模块组成，一个用于确定插入的对象掩码位置和比例，另一个用于确定对象掩码形状和姿态。两个模块由一个空间变换网络连接并联合训练，该学习过程利用了监督和无监督数据集，实验证明该模型能够在图像中的多个位置插入不同外观的物体，并具有较好的准确性。

上下文感知物体实例合成与放置

Context-Aware Synthesis and Placement of Object Instances

Recent studies show that widely used deep neural networks (DNNs) are
vulnerable to carefully crafted adversarial examples. Many advanced algorithms
have been proposed to generate adversarial examples by leveraging the
$\mathcal{L}_p$ distance for penalizing perturbations. Researchers have
explored different defense methods to defend against such adversarial attacks.
While the effectiveness of $\mathcal{L}_p$ distance as a metric of perceptual
quality remains an active research area, in this paper we will instead focus on
a different type of perturbation, namely spatial transformation, as opposed to
manipulating the pixel values directly as in prior works. Perturbations
generated through spatial transformation could result in large $\mathcal{L}_p$
distance measures, but our extensive experiments show that such spatially
transformed adversarial examples are perceptually realistic and more difficult
to defend against with existing defense systems. This potentially provides a
new direction in adversarial example generation and the design of corresponding
defenses. We visualize the spatial transformation based perturbation for
different examples and show that our technique can produce realistic
adversarial examples with smooth image deformation. Finally, we visualize the
attention of deep networks with different types of adversarial examples to
better understand how these examples are interpreted.

本文研究深度神经网络中的对抗样本问题，提出了一种新的扰动方法：利用空间变换生成对抗性样本以增强样本的感知逼真度，证明这种方法在现有防御系统方面更加具有挑战性，并通过可视化技术研究神经网络对不同类型对抗样本的感知。