What Dress Fits Me Best?

Hidayati, Shintami Chusnul; Hsu, Cheng-Chun; Chang, Yu-Ting; Hua, Kai-Lung; Fu, Jianlong; Cheng, Wen-Huang

doi:10.1145/3240508.3240546

Cited by 59 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a starting point, we analyzed over 40 publications related to the topic of cloth recognition and fashion items compatibility. These studies are focused on recognition of clothes categories [14], recommendation of compatible fashion items [13], [15], [16] and human pose estimation to make fashion recommendations. In earlier studies, generally feature engineering and manual labeling was mainly used [1], while modern approaches are based on convolutional neural networks (CNN) that automatically extract features at different levels of abstraction, including low-level features such as color and texture, and high-level features such as clothing form, fashion, and style.…”

Section: Related Workmentioning

confidence: 99%

Assessment of the color compatibility of garments for building a recommendation system for an outfit

Gerasimova,

Sholomov

2024

Sixteenth International Conference on Machine Vision (ICMV 2023)

View full text Add to dashboard Cite

In this paper, we consider the problem of clothes compatibility for total look recommendation systems by means of deep neural networks. This task has become very popular in recent years, primarily due to the growth of online retail sales of clothing. Unlike the existing solutions, we developed a comprehensive model of clothes compatibility evaluation based on color characteristics as well as on the characteristics of the style. As a rule, neural networks are robust to the color characteristics of an image, but color is an extremely important component in the task of a total look evaluation, so such additional branch with color characteristics is well justified. The proposed model uses both: color embedding obtained from color clustering and histograms, and style embedding as an output tensor of ResNet-50 encoder. The paper shows that color embeddings significantly improve the quality of the total look evaluation. The model was trained on Polyvore dataset, which was pre-processed and cleaned from the items not related to the topic of total look compatibility.

show abstract

Section: Related Workmentioning

confidence: 99%

Assessment of the color compatibility of garments for building a recommendation system for an outfit

Gerasimova,

Sholomov

2024

Sixteenth International Conference on Machine Vision (ICMV 2023)

View full text Add to dashboard Cite

show abstract

“…GCN techniques are widely used in recommendation systems [17,18,19,20,21]. In the context of video recommendation, Hamilton et al [22] introduced a general inductive framework that utilizes content information to generate node representations for unseen data.…”

Section: Related Workmentioning

confidence: 99%

Advancing Personalized Recommendation Algorithms with RNN and Multimodal Features

Neri,

Ferri,

Lombardi

2023

Preprint

View full text Add to dashboard Cite

Traditional recommendation systems, employing collaborative filtering (CF) methods, recommend related videos for a given seed by considering how many users watched a particular candidate video immediately after the seed video. This approach primarily relies on aggregate user behavior and does not take into account the content of the videos. Current recommendation systems can recommend content across different media types, including text, images, audio, and video. This is achieved by pre-tagging various media files with different labels and then using search engines to find content based on user needs. However, with the evolution of video platforms, user demands for personalization have become stronger, and recommendation scenarios have become more complex. There is an increasing need for precision in video recommendations. To address the limitations, we focus on integrating multiple modalities into recommendation systems. The main contributions of this work are: We explore how to effectively integrate the various modalities to reflect user preferences and how they affect recommendation performance. We perform extensive experiments on public dataset to demonstrate the performance of our proposed model.

show abstract

“…In determining the number of subjects, we referred to previous articles of the same type. [40][41][42][43] Furthermore, in conjunction with this article, although the steps of a systematic evaluation are simple, it requires a high degree of cooperation of the subjects and carefully judging the category of each set of collocation (satisfactory or relevant). In conclusion, this study faced 20 randomly selected testers from the major groups.…”

Section: Practical Application and Evaluationmentioning

confidence: 99%

Construction of womenswear matching expert system based on domain knowledge

Guo

et al. 2022

Textile Research Journal

View full text Add to dashboard Cite

Clothing matching refers to the coordination of style, color, etc. to achieve a decent and generous effect. With the development of artificial intelligence, increasing research efforts have been dedicated to complementary garment collocation as matching clothes to make a suitable outfit has become a daily headache for many people. However, existing studies neglect rules regarding clothing matching, which are based on the knowledge accumulated in the fashion domain and are implicitly transmitted and ambiguous. Towards this end, this article proposes an expert system based on domain knowledge implemented using the Prolog program to realize rule-guided clothing collocation. The article constructs clothing matching rules from four essential clothing attributes: season, type, style, and color, and uses Prolog syntax for knowledge representation. For the formulated facts and rules, the reasoning machine iteratively matches the user's instructions with the system knowledge to reason out suitable matching suggestions. In this study we built a website as a man-machine interface to facilitate friendly interaction and set season, type, style, and color sub-options to match clothing and satisfy user preferences. The system validation based on standard metrics (precision, recall, F1-measure) achieves results above 81%. The main contribution is that the system could match clothes more accurately according to the likes and requirements of users. After comparing with other research, the system is expected to provide suggestions or references for people to choose style and dressing.

show abstract

What Dress Fits Me Best?

Cited by 59 publications

References 31 publications

Assessment of the color compatibility of garments for building a recommendation system for an outfit

Assessment of the color compatibility of garments for building a recommendation system for an outfit

Advancing Personalized Recommendation Algorithms with RNN and Multimodal Features

Construction of womenswear matching expert system based on domain knowledge

Contact Info

Product

Resources

About