While most image captioning aims to generate objective descriptions of
images, the last few years have seen work on generating visually grounded image
captions which have a specific style (e.g., incorporating positive or negative
sentiment). However, because the stylistic component is typically the last part
of training, current models usually pay more attention to the style at the
expense of accurate content description. In addition, there is a lack of
variability in terms of the stylistic aspects. To address these issues, we
propose an image captioning model called ATTEND-GAN which has two core
components: first, an attention-based caption generator to strongly correlate
different parts of an image with different parts of a caption; and second, an
adversarial training mechanism to assist the caption generator to add diverse
stylistic components to the generated captions. Because of these components,
ATTEND-GAN can generate correlated captions as well as more human-like
variability of stylistic patterns. Our system outperforms the state-of-the-art
as well as a collection of our baseline models. A linguistic analysis of the
generated captions demonstrates that captions generated using ATTEND-GAN have a
wider range of stylistic adjectives and adjective-noun pairs.

提出一种名为 ATTEND-GAN 的图像描述模型，其利用基于注意力的描述生成器和对抗训练机制，使生成的描述具有相关性和更广泛的风格变化，证明其优于现有技术并提供更好的图像描述。

通过对抗训练生成风格化的图像标题

Towards Generating Stylized Image Captions via Adversarial Training

What defines a visual style? Fashion styles emerge organically from how
people assemble outfits of clothing, making them difficult to pin down with a
computational model. Low-level visual similarity can be too specific to detect
stylistically similar images, while manually crafted style categories can be
too abstract to capture subtle style differences. We propose an unsupervised
approach to learn a style-coherent representation. Our method leverages
probabilistic polylingual topic models based on visual attributes to discover a
set of latent style factors. Given a collection of unlabeled fashion images,
our approach mines for the latent styles, then summarizes outfits by how they
mix those styles. Our approach can organize galleries of outfits by style
without requiring any style labels. Experiments on over 100K images demonstrate
its promise for retrieving, mixing, and summarizing fashion images by their
style.

提出了一种基于概率多语言主题模型的无监督方法来发现一组潜在的风格因子，进而通过混合这些因子来对时尚图片进行风格总结，并无需任何风格标签，从而可以根据风格来组织衣服图库。