Understanding fashion images has been advanced by benchmarks with rich
annotations such as DeepFashion, whose labels include clothing categories,
landmarks, and consumer-commercial image pairs. However, DeepFashion has
nonnegligible issues such as single clothing-item per image, sparse landmarks
(4~8 only), and no per-pixel masks, making it had significant gap from
real-world scenarios. We fill in the gap by presenting DeepFashion2 to address
these issues. It is a versatile benchmark of four tasks including clothes
detection, pose estimation, segmentation, and retrieval. It has 801K clothing
items where each item has rich annotations such as style, scale, viewpoint,
occlusion, bounding box, dense landmarks and masks. There are also 873K
Commercial-Consumer clothes pairs. A strong baseline is proposed, called Match
R-CNN, which builds upon Mask R-CNN to solve the above four tasks in an
end-to-end manner. Extensive evaluations are conducted with different
criterions in DeepFashion2.

DeepFashion2 是一个多任务的数据集和基准，旨在为四个任务提供衣服检测，姿势估计，分割和检索，“Match R-CNN” 是解决这四个任务的强有力基线。

DeepFashion2：服装图像检测、姿态估计、分割和再识别的通用基准

DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation,  Segmentation and Re-Identification of Clothing Images

A picture is worth a thousand words. Albeit a clich\'e, for the fashion
industry, an image of a clothing piece allows one to perceive its category
(e.g., dress), sub-category (e.g., day dress) and properties (e.g., white
colour with floral patterns). The seasonal nature of the fashion industry
creates a highly dynamic and creative domain with evermore data, making it
unpractical to manually describe a large set of images (of products). In this
paper, we explore the concept of visual recognition for fashion images through
an end-to-end architecture embedding the hierarchical nature of the annotations
directly into the model. Towards that goal, and inspired by the work of [7], we
have modified and adapted the original architecture proposal. Namely, we have
removed the message passing layer symmetry to cope with Farfetch category tree,
added extra layers for hierarchy level specificity, and moved the message
passing layer into an enriched latent space. We compare the proposed unified
architecture against state-of-the-art models and demonstrate the performance
advantage of our model for structured multi-level categorization on a dataset
of about 350k fashion product images.

本研究通过一种端到端的架构，将层次化注释的概念直接嵌入到模型中，以实现对时尚图像的视觉识别，并针对大约 350k 时尚产品图像的数据集进行了多级结构分类的性能优化和演示。

基于结构化输出的时尚图像分类统一模型

A Unified Model with Structured Output for Fashion Images Classification

We introduce a new dataset of 293,008 high definition (1360 x 1360 pixels)
fashion images paired with item descriptions provided by professional stylists.
Each item is photographed from a variety of angles. We provide baseline results
on 1) high-resolution image generation, and 2) image generation conditioned on
the given text descriptions. We invite the community to improve upon these
baselines. In this paper, we also outline the details of a challenge that we
are launching based upon this dataset.

本文介绍了一个由职业造型师提供的 293,008 张高清时尚图像和物品说明组成的数据集。我们在高分辨率图像生成和给定文本条件下的图像生成方面提供基准结果，并概述了一个基于此数据集的挑战的细节。

Fashion-Gen: 时尚生成数据集与挑战

Fashion-Gen: The Generative Fashion Dataset and Challenge

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。