Neural style transfer has drawn broad attention in recent years. However,
most existing methods aim to explicitly model the transformation between
different styles, and the learned model is thus not generalizable to new
styles. We here attempt to separate the representations for styles and
contents, and propose a generalized style transfer network consisting of style
encoder, content encoder, mixer and decoder. The style encoder and content
encoder are used to extract the style and content factors from the style
reference images and content reference images, respectively. The mixer employs
a bilinear model to integrate the above two factors and finally feeds it into a
decoder to generate images with target style and content. To separate the style
features and content features, we leverage the conditional dependence of styles
and contents given an image. During training, the encoder network learns to
extract styles and contents from two sets of reference images in limited size,
one with shared style and the other with shared content. This learning
framework allows simultaneous style transfer among multiple styles and can be
deemed as a special `multi-task' learning scenario. The encoders are expected
to capture the underlying features for different styles and contents which is
generalizable to new styles and contents. For validation, we applied the
proposed algorithm to the Chinese Typeface transfer problem. Extensive
experiment results on character generation have demonstrated the effectiveness
and robustness of our method.

提出一种广义的神经风格转移网络，由风格解码器、内容解码器、混合器和解码器组成，可用于多种风格的图像转移任务。

泛化风格迁移中分离风格与内容

Separating Style and Content for Generalized Style Transfer

Three dimensional convolutional neural networks (3D CNNs) have been
established as a powerful tool to simultaneously learn features from both
spatial and temporal dimensions, which is suitable to be applied to video-based
action recognition. In this work, we propose not to directly use the
activations of fully-connected layers of a 3D CNN as the video feature, but to
use selective convolutional layer activations to form a discriminative
descriptor for video. It pools the feature on the convolutional layers under
the guidance of body joint positions. Two schemes of mapping body joints into
convolutional feature maps for pooling are discussed. The body joint positions
can be obtained from any off-the-shelf skeleton estimation algorithm. The
helpfulness of the body joint guided feature pooling with inaccurate skeleton
estimation is systematically evaluated. To make it end-to-end and do not rely
on any sophisticated body joint detection algorithm, we further propose a
two-stream bilinear model which can learn the guidance from the body joints and
capture the spatio-temporal features simultaneously. In this model, the body
joint guided feature pooling is conveniently formulated as a bilinear product
operation. Experimental results on three real-world datasets demonstrate the
effectiveness of body joint guided pooling which achieves promising
performance.

该研究提出了一种利用卷积层激活函数形成判别视频描述符的方法，在身体关节位置的指引下对卷积层数上的特征进行聚合，从而提升视频识别的准确性，并使用双线性模型来使其端到端化和不依赖于任何复杂的身体关节检测算法。

基于身体关节引导的 3D 卷积深度描述符用于动作识别

Body Joint guided 3D Deep Convolutional Descriptors for Action  Recognition

Generating a novel textual description of an image is an interesting problem
that connects computer vision and natural language processing. In this paper,
we present a simple model that is able to generate descriptive sentences given
a sample image. This model has a strong focus on the syntax of the
descriptions. We train a purely bilinear model that learns a metric between an
image representation (generated from a previously trained Convolutional Neural
Network) and phrases that are used to described them. The system is then able
to infer phrases from a given image sample. Based on caption syntax statistics,
we propose a simple language model that can produce relevant descriptions for a
given test image using the phrases inferred. Our approach, which is
considerably simpler than state-of-the-art models, achieves comparable results
on the recently release Microsoft COCO dataset.

该文介绍了一种使用自然语言描述图像的算法，该算法结合了计算机视觉和自然语言处理。通过使用先前训练好的卷积神经网络生成的图像表示和用于描述它们的短语之间的度量，该纯双线性模型训练出能够生成描述语句的能力。同时，作者提出了一种简单的语言模型，该模型能够针对给定的测试图像生成相关的描述。实验结果表明，该方法可以在最近发布的 Microsoft COCO 数据集上获得与先进模型相当的结果，而模型极为简单。