79 research outputs found
Toward Explainable Fashion Recommendation
Many studies have been conducted so far to build systems for recommending
fashion items and outfits. Although they achieve good performances in their
respective tasks, most of them cannot explain their judgments to the users,
which compromises their usefulness. Toward explainable fashion recommendation,
this study proposes a system that is able not only to provide a goodness score
for an outfit but also to explain the score by providing reason behind it. For
this purpose, we propose a method for quantifying how influential each feature
of each item is to the score. Using this influence value, we can identify which
item and what feature make the outfit good or bad. We represent the image of
each item with a combination of human-interpretable features, and thereby the
identification of the most influential item-feature pair gives useful
explanation of the output score. To evaluate the performance of this approach,
we design an experiment that can be performed without human annotation; we
replace a single item-feature pair in an outfit so that the score will
decrease, and then we test if the proposed method can detect the replaced item
correctly using the above influence values. The experimental results show that
the proposed method can accurately detect bad items in outfits lowering their
scores
Computational Technologies for Fashion Recommendation: A Survey
Fashion recommendation is a key research field in computational fashion
research and has attracted considerable interest in the computer vision,
multimedia, and information retrieval communities in recent years. Due to the
great demand for applications, various fashion recommendation tasks, such as
personalized fashion product recommendation, complementary (mix-and-match)
recommendation, and outfit recommendation, have been posed and explored in the
literature. The continuing research attention and advances impel us to look
back and in-depth into the field for a better understanding. In this paper, we
comprehensively review recent research efforts on fashion recommendation from a
technological perspective. We first introduce fashion recommendation at a macro
level and analyse its characteristics and differences with general
recommendation tasks. We then clearly categorize different fashion
recommendation efforts into several sub-tasks and focus on each sub-task in
terms of its problem formulation, research focus, state-of-the-art methods, and
limitations. We also summarize the datasets proposed in the literature for use
in fashion recommendation studies to give readers a brief illustration.
Finally, we discuss several promising directions for future research in this
field. Overall, this survey systematically reviews the development of fashion
recommendation research. It also discusses the current limitations and gaps
between academic research and the real needs of the fashion industry. In the
process, we offer a deep insight into how the fashion industry could benefit
from fashion recommendation technologies. the computational technologies of
fashion recommendation
A Bibliometric Survey of Fashion Analysis using Artificial Intelligence
In the 21st century, clothing fashion has become an inevitable part of every individual human as it is considered a way to express their personality to the outside world. Currently the traditional fashion business models are experiencing a paradigm shift from being an experience-based business strategy implementation to a data driven intelligent business improvisation. Artificial Intelligence is acting as a catalyst to achieve the infusion of data intelligence into the fashion industry which aims at fostering all the business brackets such as supply chain management, trend analysis, fashion recommendation, sales forecasting, digitized shopping experience etc. The field of “Fashion AI\u27\u27 is still under research progress because the fashion data is a multifaceted entity which is available in any of the forms like an image, video, text and numerical values. Therefore, it becomes a challenging research arena. There is a paucity of a common study which can provide a bird’s eye view about the research efforts and directions. In this paper, the authors represent a bibliometric survey of the AI based fashion analysis domain based on the Scopus database. The study was conducted by retrieving 581 Scopus research papers published from 1975-2020 and analysed to find out critical insights such as publication volume, co-authorship networks, citation analysis, and demographic research distribution. The study revealed that significant contribution is made via concept propositions in conferences and some papers published in the journal. However, there is a scope of lots of research work in the direction of improving fashion industry with AI techniques
A Bibliometric Survey of Fashion Analysis using Artificial Intelligence
In the 21st century, clothing fashion has become an inevitable part of every individual human as it is considered a way to express their personality to the outside world. Currently the traditional fashion business models are experiencing a paradigm shift from being an experience-based business strategy implementation to a data driven intelligent business improvisation. Artificial Intelligence is acting as a catalyst to achieve the infusion of data intelligence into the fashion industry which aims at fostering all the business brackets such as supply chain management, trend analysis, fashion recommendation, sales forecasting, digitized shopping experience etc. The field of “Fashion AI\u27\u27 is still under research progress because the fashion data is a multifaceted entity which is available in any of the forms like an image, video, text and numerical values. Therefore, it becomes a challenging research arena. There is a paucity of a common study which can provide a bird’s eye view about the research efforts and directions. In this paper, the authors represent a bibliometric survey of the AI based fashion analysis domain based on the Scopus database. The study was conducted by retrieving 581 Scopus research papers published from 1975-2020 and analysed to find out critical insights such as publication volume, co-authorship networks, citation analysis, and demographic research distribution. The study revealed that significant contribution is made via concept propositions in conferences and some papers published in the journal. However, there is a scope of lots of research work in the direction of improving fashion industry with AI techniques
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