In this paper, we introduce an unsupervised learning approach to
automatically discover, summarize, and manipulate artistic styles from large
collections of paintings. Our method is based on archetypal analysis, which is
an unsupervised learning technique akin to sparse coding with a geometric
interpretation. When applied to deep image representations from a collection of
artworks, it learns a dictionary of archetypal styles, which can be easily
visualized. After training the model, the style of a new image, which is
characterized by local statistics of deep visual features, is approximated by a
sparse convex combination of archetypes. This enables us to interpret which
archetypal styles are present in the input image, and in which proportion.
Finally, our approach allows us to manipulate the coefficients of the latent
archetypal decomposition, and achieve various special effects such as style
enhancement, transfer, and interpolation between multiple archetypes.

本研究提出一种无监督学习方法，利用原型分析从大量文艺作品中发现、总结和操作艺术风格，实现模型训练后的风格操作与转移。

原型风格分析下的艺术风格无监督学习

Unsupervised Learning of Artistic Styles with Archetypal Style Analysis

Text in natural images contains rich semantics that are often highly relevant
to objects or scene. In this paper, we focus on the problem of fully exploiting
scene text for visual understanding. The main idea is combining word
representations and deep visual features into a globally trainable deep
convolutional neural network. First, the recognized words are obtained by a
scene text reading system. Then, we combine the word embedding of the
recognized words and the deep visual features into a single representation,
which is optimized by a convolutional neural network for fine-grained image
classification. In our framework, the attention mechanism is adopted to reveal
the relevance between each recognized word and the given image, which further
enhances the recognition performance. We have performed experiments on two
datasets: Con-Text dataset and Drink Bottle dataset, that are proposed for
fine-grained classification of business places and drink bottles, respectively.
The experimental results consistently demonstrate that the proposed method
combining textual and visual cues significantly outperforms classification with
only visual representations. Moreover, we have shown that the learned
representation improves the retrieval performance on the drink bottle images by
a large margin, making it potentially useful in product search.

本文提出了一种利用场景文本进行图像理解的方法，通过符号识别系统获取识别的单词和深层视觉特征的嵌入组合成一个可通过卷积神经网络进行优化的单一表示形式。通过引入注意力机制强化符号与图像之间的关系，不仅有效地提高分类准确率，而且可较大幅度提升产品图像检索性能。

融合场景文本与视觉外观进行细粒度图像分类

Integrating Scene Text and Visual Appearance for Fine-Grained Image  Classification

In some of object recognition problems, labeled data may not be available for
all categories. Zero-shot learning utilizes auxiliary information (also called
signatures) describing each category in order to find a classifier that can
recognize samples from categories with no labeled instance. In this paper, we
propose a novel semi-supervised zero-shot learning method that works on an
embedding space corresponding to abstract deep visual features. We seek a
linear transformation on signatures to map them onto the visual features, such
that the mapped signatures of the seen classes are close to labeled samples of
the corresponding classes and unlabeled data are also close to the mapped
signatures of one of the unseen classes.
We use the idea that the rich deep visual features provide a representation
space in which samples of each class are usually condensed in a cluster. The
effectiveness of the proposed method is demonstrated through extensive
experiments on four public benchmarks improving the state-of-the-art prediction
accuracy on three of them.

本文提出了一种基于深度可视特征嵌入空间的半监督零样本学习方法，通过在签名上进行线性变换以将它们映射到视觉特征上，使得已知类别的签名映射结果靠近相应类别的已标记样本，同时未标记的数据与其中一个未知类别的映射签名也很接近，该方法在四个公共基准测试中的表现证明了其有效性，其中三个的预测准确度均优于最先进方法。