Due to the huge category number, the sophisticated combinations of various
strokes and radicals, and the free writing or printing styles, generating
Chinese characters with diverse styles is always considered as a difficult
task. In this paper, an efficient and generalized deep framework, namely, the
W-Net, is introduced for the one-shot arbitrary-style Chinese character
generation task. Specifically, given a single character (one-shot) with a
specific style (e.g., a printed font or hand-writing style), the proposed W-Net
model is capable of learning and generating any arbitrary characters sharing
the style similar to the given single character. Such appealing property was
rarely seen in the literature. We have compared the proposed W-Net framework to
many other competitive methods. Experimental results showed the proposed method
is significantly superior in the one-shot setting.

通过引入 W-Net 模型，本文提出了一种高效和广义的深度框架，可用于一次过任意风格的汉字生成任务。实验结果表明，该方法在一次生成的设置下明显优于其他竞争方法。

W-Net：基于深度神经网络的一次成像任意风格汉字生成

W-Net: One-Shot Arbitrary-Style Chinese Character Generation with Deep  Neural Networks

Emotion recognition is relevant for human behaviour understanding, where
facial expression and speech recognition have been widely explored by the
computer vision community. Literature in the field of behavioural psychology
indicates that gait, described as the way a person walks, is an additional
indicator of emotions. In this work, we propose a deep framework for emotion
recognition through the analysis of gait. More specifically, our model is
composed of a sequence of spatial-temporal Graph Convolutional Networks that
produce a robust skeleton-based representation for the task of emotion
classification. We evaluate our proposed framework on the E-Gait dataset,
composed of a total of 2177 samples. The results obtained represent an
improvement of approximately 5% in accuracy compared to the state of the art.
In addition, during training we observed a faster convergence of our model
compared to the state-of-the-art methodologies.

通过分析步态，我们提出了一个深度框架用于情感识别，该模型由一系列空间 - 时间图卷积网络组成，用于生成稳健的基于骨架的情感分类任务的表示，评估结果显示相对于现有技术，准确率提高了约 5%，此外，我们观察到训练期间我们的模型比现有技术方法收敛更快。

ST-Gait++：基于时空卷积的视频步态情绪识别

ST-Gait++: Leveraging spatio-temporal convolutions for gait-based  emotion recognition on videos

This paper proposes the first non-flow-based deep framework for high dynamic
range (HDR) imaging of dynamic scenes with large-scale foreground motions. In
state-of-the-art deep HDR imaging, input images are first aligned using optical
flows before merging, which are still error-prone due to occlusion and large
motions. In stark contrast to flow-based methods, we formulate HDR imaging as
an image translation problem without optical flows. Moreover, our simple
translation network can automatically hallucinate plausible HDR details in the
presence of total occlusion, saturation and under-exposure, which are otherwise
almost impossible to recover by conventional optimization approaches. Our
framework can also be extended for different reference images. We performed
extensive qualitative and quantitative comparisons to show that our approach
produces excellent results where color artifacts and geometric distortions are
significantly reduced compared to existing state-of-the-art methods, and is
robust across various inputs, including images without radiometric calibration.

该研究提出了一种用于高动态范围成像的非基于流的深度框架，其中将 HDR 成像作为一种图像翻译问题，提出了一种新的算法，从而减少光学流错位、理光、动态物体和一些曝光问题带来的挑战，并与现有算法相比取得了卓越的结果。