Natural question generation (QG) aims to generate questions from a passage
and an answer. Previous works on QG either (i) ignore the rich structure
information hidden in text, (ii) solely rely on cross-entropy loss that leads
to issues like exposure bias and inconsistency between train/test measurement,
or (iii) fail to fully exploit the answer information. To address these
limitations, in this paper, we propose a reinforcement learning (RL) based
graph-to-sequence (Graph2Seq) model for QG. Our model consists of a Graph2Seq
generator with a novel Bidirectional Gated Graph Neural Network based encoder
to embed the passage, and a hybrid evaluator with a mixed objective combining
both cross-entropy and RL losses to ensure the generation of syntactically and
semantically valid text. We also introduce an effective Deep Alignment Network
for incorporating the answer information into the passage at both the word and
contextual levels. Our model is end-to-end trainable and achieves new
state-of-the-art scores, outperforming existing methods by a significant margin
on the standard SQuAD benchmark.

本篇文章提出了基于强化学习 (Graph2Seq) 模型的问题生成方法，该模型利用双向门控图神经网络作为 encoder 嵌入文章，并混合应用交叉熵损失和 RL 损失来确保生成语法和语义合理的文本。此外，文章还介绍了一种有效的深度对齐网络，将答案信息融合到文章中。该模型的端到端训练在标准 SQuAD 基准测试中取得了明显的最优成绩，超过了现有方法。

基于强化学习的图转序列模型用于自然语言问题生成

Reinforcement Learning Based Graph-to-Sequence Model for Natural  Question Generation

The topic of facial landmark detection has been widely covered for pictures
of human faces, but it is still a challenge for drawings. Indeed, the
proportions and symmetry of standard human faces are not always used for comics
or mangas. The personal style of the author, the limitation of colors, etc.
makes the landmark detection on faces in drawings a difficult task. Detecting
the landmarks on manga images will be useful to provide new services for easily
editing the character faces, estimating the character emotions, or generating
automatically some animations such as lip or eye movements.
This paper contains two main contributions: 1) a new landmark annotation
model for manga faces, and 2) a deep learning approach to detect these
landmarks. We use the "Deep Alignment Network", a multi stage architecture
where the first stage makes an initial estimation which gets refined in further
stages. The first results show that the proposed method succeed to accurately
find the landmarks in more than 80% of the cases.

本文提出一种基于 Deep Alignment Network 深度学习方法的漫画面部标记检测新模型，成功地在 80％以上的情况下检测到了面部特征点。

漫画图像的面部特征点检测

Facial Landmark Detection for Manga Images

Classification of human emotions remains an important and challenging task
for many computer vision algorithms, especially in the era of humanoid robots
which coexist with humans in their everyday life. Currently proposed methods
for emotion recognition solve this task using multi-layered convolutional
networks that do not explicitly infer any facial features in the classification
phase. In this work, we postulate a fundamentally different approach to solve
emotion recognition task that relies on incorporating facial landmarks as a
part of the classification loss function. To that end, we extend a recently
proposed Deep Alignment Network (DAN), that achieves state-of-the-art results
in the recent facial landmark recognition challenge, with a term related to
facial features. Thanks to this simple modification, our model called
EmotionalDAN is able to outperform state-of-the-art emotion classification
methods on two challenging benchmark dataset by up to 5%.

该研究提出了一种基于面部标志的情感识别分类损失函数修改方法，将一种新方法 EmotionalDAN 应用于情感识别上，并在两个挑战性基准数据集上实现了比最先进的情感分类方法高达 5% 的准确度。

面部标志识别的情感识别

I Know How You Feel: Emotion Recognition with Facial Landmarks

In this paper, we propose Deep Alignment Network (DAN), a robust face
alignment method based on a deep neural network architecture. DAN consists of
multiple stages, where each stage improves the locations of the facial
landmarks estimated by the previous stage. Our method uses entire face images
at all stages, contrary to the recently proposed face alignment methods that
rely on local patches. This is possible thanks to the use of landmark heatmaps
which provide visual information about landmark locations estimated at the
previous stages of the algorithm. The use of entire face images rather than
patches allows DAN to handle face images with large variation in head pose and
difficult initializations. An extensive evaluation on two publicly available
datasets shows that DAN reduces the state-of-the-art failure rate by up to 70%.
Our method has also been submitted for evaluation as part of the Menpo
challenge.

本文提出了一种基于深度神经网络结构的鲁棒人脸对齐方法 Deep Alignment Network (DAN)，DAN 采用全脸图像进行人脸对齐而非局部补丁，并通过在算法的每个阶段利用地标热图来提供之前阶段的视觉信息，从而使 DAN 处理具有大头姿态变化和困难初始化的面孔图像。我们在两个公开数据集上进行了广泛评估，结果表明 DAN 将失败率的最新技术水平降低了高达 70%。