智能时尚计算机视觉技术：深度探索与应用综述

计算机视觉

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"这篇综述论文深入探讨了智能时尚与计算机视觉技术的融合，由WEN-HUANG CHENG、SIJIE SONG、CHIEH-YUN CHEN、SHINTA MICHUSNUL HIDAYATI和JIAYING LIU等人撰写，涉及四大主题：时尚检测、时尚分析、时尚合成和时尚推荐。" 在计算机视觉领域，智能时尚正逐渐成为研究热点。这篇论文详细总结了超过200项关键研究，旨在推动智能时尚的发展。首先，时尚检测是该领域的基础，包括地标检测（识别服装上的特定特征）、时尚解析（理解衣物的结构和元素）和物品检索（找到与给定图像相似的服装）。这些技术使计算机能够理解和识别不同类型的时尚元素。其次，时尚分析涵盖了属性识别（识别衣物的颜色、样式等属性）、风格学习（理解并模仿时尚趋势）以及流行度预测（预测某款服装或风格的受欢迎程度）。这些分析有助于品牌和零售商做出市场决策，适应快速变化的时尚潮流。第三部分，时尚合成主要涉及风格转移（将一种风格应用于另一种图像）、姿势变换（将衣物从一个姿势迁移到另一个姿势）和物理模拟（确保虚拟衣物在不同动态条件下的真实感）。这些技术在虚拟试衣间和在线购物体验中起着关键作用，提供更逼真的预览效果。最后，时尚推荐系统是智能时尚中的一个重要组成部分，包括时尚兼容性分析（判断两件或多件衣物是否搭配）、 outfit匹配（创建协调的服装组合）和发型建议（根据个人特征推荐适合的发型）。这些推荐系统利用机器学习算法，为用户提供个性化的时尚指导，提升购物体验。这篇论文全面概述了智能时尚计算机视觉技术的现状和进展，对研究者和业界人员了解这一领域的最新动态具有很高的参考价值。随着技术的不断进步，我们可以期待智能时尚在零售、广告、娱乐等领域发挥更大的作用，改变我们与时尚互动的方式。

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Fashion Meets Computer Vision: A Survey 7

Table 3. Summary of the benchmark datasets for fashion parsing task.

Dataset name

Publish

time

# of

photos

# of

classes

Key features

Sources

Fashionista dataset [206]

2012 158,235 56

Annotated with tags, comments, and links Chictopia.com

Daily Photos (DP) [32]

2013 2,500 18

High resolution images; Parselet deﬁnition Chictopia.com

Paper Doll dataset [205, 207]

2013 339,797 56

Annotated with metadata tags denoting characteris-

tics, e.g., color, style, occasion, clothing type, brand

Fashionista [206],

Chictopia.com

Clothing Co-Parsing (CCP)

SYSU-Clothes [106, 212]

2014 2,098 57

Annotated with superpixel-level or image-level tags Online shopping websites

Colorful-Fashion Dataset

(CFD) [116]

2014 2,682 23

Annotated with 13 colors Chictopia.com

ATR [107]

Benchmark

2015

5,867

Standing people in frontal/near-frontal view with good

visibilities of all body parts

Fashionista [206], Daily

Photos [32], CFD [116]

Human Parsing

in the Wild

1,833

Annotated with pixel-level labels N/A

Chictopia10k [108]

2015 10,000 18

It contains real-world images with arbitrary postures,

views and backgrounds

Chictopia.com

LIP [41, 105]

2017 50,462 20

Annotated with pixel-wise and body joints Microsoft COCO [111]

PASCAL-Person-Part [196]

2017 3,533 14

It contains multiple humans per image in uncon-

strained poses and occlusions

N/A

MHP

v1.0 [98]

2017 4,980 18

There are 7 body parts and 11 clothes and accessory

categories

N/A

Crowd Instance-level Human

Parsing (CIHP) [40]

2018 38,280 19

Multiple-person images; pixel-wise annotations in

instance-level

Google, Bing

ModaNet [227]

2018 55,176 13

Annotated with pixel-level labels, bounding boxes,

and polygons

PaperDoll [205]

DeepFashion2 [38]

2019 491,000 13

A versatile benchmark of four tasks including clothes

detection, pose estimation, segmentation, and retrieval.

DeepFashion [123],

Online shopping websites

Fashionpedia [80]

2020 48,000 46

There are 294 ﬁne-grained attributes. It contains high

resolution with 1710 × 2151 to maintain more details

Flickr, Free license

photo websites

N/A: there is no reported information to cite

human parsing. This work won the 1

𝑠𝑡

place within all three human parsing tracks in the 2

𝑛𝑑

Look

Into Person (LIP) Challenge

. For multi-person parsing, they designed a global to local prediction

process based on CE2P cooperating with Mask R-CNN to form M-CE2P framework and achieved

the multi-person parsing goal.

In 2019, hierarchical graph was considered for human parsing tasks [

187

]. Wang et al. [

187

]

deﬁned the human body as a hierarchy of multi-level semantic parts and employed three processes

(direct, top-down, and bottom-up) to capture the human parsing information for better parsing

performance. For tackling human parsing in various domain via a single model without retraining

on various datasets, Gong et al. [

] comprised hierarchical graph transfer learning based on the

conventional parsing network to constitute a general human parsing model, Graphonomy

, which

consisted of two processes. It ﬁrst learned and propagated compact high-level graph representation

among the labels within one dataset, and then transferred semantic information across multiple

datasets.

2.2.2 Benchmark Datasets. There are multiple datasets for fashion parsing, most of which are

collected from Chictopia

, a social networking website for fashion bloggers. Table 3 summarizes

the benchmark datasets for fashion parsing in more detail. To date, the most comprehensive one is

the LIP dataset [

105

], containing over 50,000 annotated images with 19 semantic part labels

captured from a wider range of viewpoints, occlusions, and background complexity.

2.2.3 Performance Evaluations. There are multiple metrics for evaluating fashion parsing meth-

ods: (1) average Pixel Accuracy (aPA) as the proportion of correctly labeled pixels in the whole

image, (2) mean Average Garment Recall (mAGR), (3) Intersection over Union (IoU) as the ratio of

https://vuhcs.github.io/vuhcs-2018/index.html

https://github.com/Gaoyiminggithub/Graphonomy

http://chictopia.com

, Vol. 1, No. 1, Article . Publication date: January 2021.

8 W.-H. Cheng et al.

the overlapping area of the ground truth and predicted area to the total area, (4) mean accuracy, (5)

average precision, (6) average recall, (7) average F-1 score over pixels, and (8) foreground accuracy

as the number of true pixels on the body over the number of actual pixels on the body.

In particular, most of the parsing methods are evaluated on Fashionista dataset [

206

] in terms

of accuracy, average precision, average recall, and average F-1 score over pixels. We report the

performance comparisons in Table 1 of Supplementary Material.

2.3 Item Retrieval

As fashion e-commerce has grown over the years, there has been a high demand for innovative

solutions to help customers ﬁnd preferred fashion items with ease. Though many fashion online

shopping sites support keyword-based searches, there are many visual traits of fashion items that are

not easily translated into words. It thus attracts tremendous attention from many research communities

to develop cross-scenario image-based fashion retrieval tasks for matching the real-world fashion

items to the online shopping image. Given a fashion image query, the goal of image-based fashion

item retrieval is to ﬁnd similar or identical items from the gallery.

2.3.1 State-of-the-art methods. The notable early work on automatic image-based clothing

retrieval was presented by Wang and Zhang [

190

]. To date, extensive research studies have been

devoted to addressing the problem of cross-scenario clothing retrieval since there is a large domain

discrepancy between daily human photo captured in general environment and clothing images taken

in ideal conditions (i.e., embellished photos used in online clothing shops). Liu et al. [

122

] proposed

to utilize an unsupervised transfer learning method based on part-based alignment and features

derived from the sparse reconstruction. Kalantidis et al. [

] presented clothing retrieval from the

perspective of human parsing. A prior probability map of the human body was obtained through

pose estimation to guide clothing segmentation, and then the segments were classiﬁed through

locality-sensitive hashing. The visually similar items were retrieved by summing up the overlap

similarities. Notably, [86, 122, 190] are based on hand-crafted features.

With the advances of deep learning, there has been a trend of building deep neural network

architectures to solve the clothing retrieval task. Huang et al. [

] developed a Dual Attribute-

aware Ranking Network (DARN) to represent in-depth features using attribute-guided learning.

DARN simultaneously embedded semantic attributes and visual similarity constraints into the feature

learning stage, while at the same time modeling the discrepancy between domains. Li et al. [

103

]

presented a hierarchical super-pixel fusion algorithm for obtaining the intact query clothing item

and used sparse coding for improving accuracy. More explicitly, an over-segmentation hierarchical

fusion algorithm with human pose estimation was utilized to get query clothing items and to retrieve

similar images from the product clothing dataset.

The abovementioned studies are designed for similar fashion item retrieval. But more often, people

desire to ﬁnd the same fashion item, as illustrated in Fig. 4(a). The ﬁrst attempt on this task was

achieved by Kiapour et al. [

], who developed three different methods for retrieving the same

fashion item in the real-world image from the online shop, the street-to-shop retrieval task. The

three methods contained two deep learning baseline methods, and one method aimed to learn the

similarity between two different domains, street and shop domains. For the same goal of learning

the deep feature representation, Wang et al. [

188

] adopted a Siamese network that contained two

copies of the Inception-6 network with shared weights. Also, they introduced a robust contrastive

loss to alleviate over-ﬁtting caused by some positive pairs (containing the same product) that were

visually different, and used one multi-task ﬁne-tuning approach to learn a better feature representation

by tuning the parameters of the siamese network with product images and general images from

ImageNet [

]. Further, Jiang et al. [

] extended the one-way problem, street-to-shop retrieval task,

, Vol. 1, No. 1, Article . Publication date: January 2021.

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