Fashion Meets Computer Vision

Cheng, Wen-Huang; Song, Sijie; Chen, Chieh-Yun; Hidayati, Shintami Chusnul; Liu, Jiaying

doi:10.1145/3447239

Cited by 126 publications

(56 citation statements)

References 221 publications

(244 reference statements)

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“…We have also identified surveys [29,170] where the authors present a literature review of techniques at the intersection of fashion and computer vision (CV) and/or natural language processing (NLP). While we find these works relevant to this article, they remain largely different from the review presented here as those systems are not focused on RS but on other aspects of the fashion domain, such as text generation from images or pose estimation.…”

Section: Related Surveysmentioning

confidence: 99%

“…A general classification of CV in Fashion RS is presented by Cheng et al [29], according to (i) fashion detection (landmark detection, fashion parsing), (ii) fashion analysis (attribute prediction, style learning, popularity prediction), and (iii) fashion synthesis (style transfer, pose estimation). Fashion detection is a popular topic since most fashion CV techniques require performing fashion detection in the first place.…”

Section: Computer Vision and Fashion Recommender Systemsmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Modern Fashion Recommender Systems

Deldjoo¹,

Nazary²,

Ramisa³

et al. 2022

Preprint

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The textile and apparel industries have grown tremendously over the last years. Customers no longer have to visit many stores, stand in long queues, or try on garments in dressing rooms as millions of products are now available in online catalogs. However, given the plethora of options available, an effective recommendation system is necessary to properly sort, order, and communicate relevant product material or information to users. Effective fashion RS can have a noticeable impact on billions of customers' shopping experiences and increase sales and revenues on the provider-side.The goal of this survey is to provide a review of recommender systems that operate in the specific vertical domain of garment and fashion products. We have identified the most pressing challenges in fashion RS research and created a taxonomy that categorizes the literature according to the objective they are trying to accomplish (e.g., item or outfit recommendation, size recommendation, explainability, among others) and type of side-information (users, items, context). We have also identified the most important evaluation goals and perspectives (outfit generation, outfit recommendation, pairing recommendation, and fill-in-the-blank outfit compatibility prediction) and the most commonly used datasets and evaluation metrics. CCS Concepts: • Information systems → Recommender systems; Multimedia and multimodal retrieval; • Human-centered computing → User models.

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Section: Related Surveysmentioning

confidence: 99%

Section: Computer Vision and Fashion Recommender Systemsmentioning

confidence: 99%

A Review of Modern Fashion Recommender Systems

Deldjoo¹,

Nazary²,

Ramisa³

et al. 2022

Preprint

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“…The research works related to us are mainly in the field of fashion trend analysis about style learning. The key issue in this field is thus how to analyze discriminative features for different styles and also learn what style makes a trend [10]. Pioneering works [17] presented an algorithm based on the stylistic coherent and unique characteristics to automatically discover visual style elements representing fashion trends for a certain season of New York.…”

Section: Related Workmentioning

confidence: 99%

Community Trend Prediction on Heterogeneous Graph in E-commerce

Yuan

Zou

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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In online shopping, ever-changing fashion trends make merchants need to prepare more differentiated products to meet the diversified demands, and e-commerce platforms need to capture the market trend with a prophetic vision. For the trend prediction, the attribute tags, as the essential description of items, can genuinely reflect the decision basis of consumers. However, few existing works explore the attribute trend in the specific community for e-commerce. In this paper, we focus on the community trend prediction on the item attribute and propose a unified framework that combines the dynamic evolution of two graph patterns to predict the attribute trend in a specific community. Specifically, we first design a communityattribute bipartite graph at each time step to learn the collaboration of different communities. Next, we transform the bipartite graph into a hypergraph to exploit the associations of different attribute tags in one community. Lastly, we introduce a dynamic evolution component based on the recurrent neural networks to capture the fashion trend of attribute tags. Extensive experiments on three real-world datasets in a large e-commerce platform show the superiority of the proposed approach over several strong alternatives and demonstrate the ability to discover the community trend in advance. CCS CONCEPTS• Applied computing → Electronic commerce; • Information systems → Evaluation of retrieval results.

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“…In the literature, a little number of fashion surveys are proposed [6,42,53,71]. Recently, a summary on intelligent clothing analysis was made by Liu et al [42].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, Song and Mei [71] presented on overview of fashion development with the emergence with multimedia. Then, a general survey designs the whole picture of intelligent fashion without taken a specific issue [6]. Another survey [53] is proposed to present AI applications in the fashion apparel industry, but it is based only on the structured task-based multi-label classification works.…”

Section: Introductionmentioning

confidence: 99%

You can try without visiting: a comprehensive survey on virtually try-on outfits

Ghodhbani

Néji

Razzak

et al. 2022

Multimed Tools Appl

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Since the last years and until now, technology has made fast progress for many industries, in particularly, garment industry which aims to follow consumer desires and demands. One of these demands is to fit clothes before purchasing them on-line. Therefore, many research works have been focused on how to develop an intelligent apparel industry to ensure the online shopping experience. Image-based virtual try-on is among the most potential approach of virtual fitting that tries on target clothes into customer's image, therefore, it has received considerable research efforts in the recent years. However, there are several challenges involved in development of virtual try-on that make it difficult to achieve naturally looking virtual outfit such as shape, pose, occlusion, illumination cloth texture, logo and text etc. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of virtual try-on. This review first introduces virtual try-on and its challenges followed by its demand in fashion industry. We summarize state-of-the-art image based virtual try-on for both fashion detection and fashion synthesis as well as their respective advantages, drawbacks, and guidelines for selection of specific try-on model followed by its recent development and successful application. Finally, we conclude the paper with promising directions for future research.

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Fashion Meets Computer Vision

Cited by 126 publications

References 221 publications

A Review of Modern Fashion Recommender Systems

A Review of Modern Fashion Recommender Systems

Community Trend Prediction on Heterogeneous Graph in E-commerce

You can try without visiting: a comprehensive survey on virtually try-on outfits

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