25 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
ファッションのための深層学習:服装の統一性評価と格付けおよび推薦
Tohoku University岡谷貴之課
Computational Aesthetics for Fashion
The online fashion industry is growing fast and with it, the need for advanced systems able to automatically solve different tasks in an accurate way. With the rapid advance of digital technologies, Deep Learning has played an important role in Computational Aesthetics, an interdisciplinary area that tries to bridge fine art, design, and computer science. Specifically, Computational Aesthetics aims to automatize human aesthetic judgments with computational methods. In this thesis, we focus on three applications of computer vision in fashion, and we discuss how Computational Aesthetics helps solve them accurately
Data Interpretation Based on Embedded Data Representation Models : Analytical Models for Effective Online Marketing in the Fashion Industry
早稲田大学博士(工学)早大学位記番号:新9378doctoral thesi
Degradation stage classification via interpretable feature learning
Predictive maintenance (PdM) advocates for the usage of machine learning technologies to monitor asset's health conditions and plan maintenance activities accordingly. However, according to the specific degradation process, some health-related measures (e.g. temperature) may be not informative enough to reliably assess the health stage. Moreover, each measure needs to be properly treated to extract the information linked to the health stage. Those issues are usually addressed by performing a manual feature engineering, which results in high management cost and poor generalization capability of those approaches. In this work, we address this issue by coupling a health stage classifier with a feature learning mechanism. With feature learning, minimally processed data are automatically transformed into informative features. Many effective feature learning approaches are based on deep learning. With those, the features are obtained as a non-linear combination of the inputs, thus it is difficult to understand the input's contribution to the classification outcome and so the reasoning behind the model. Still, these insights are increasingly required to interpret the results and assess the reliability of the model. In this regard, we propose a feature learning approach able to (i) effectively extract high-quality features by processing different input signals, and (ii) provide useful insights about the most informative domain transformations (e.g. Fourier transform or probability density function) of the input signals (e.g. vibration or temperature). The effectiveness of the proposed approach is tested with publicly available real-world datasets about bearings' progressive deterioration and compared with the traditional feature engineering approach