3 research outputs found
PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability
Fashion is an important part of human experience. Events such as interviews,
meetings, marriages, etc. are often based on clothing styles. The rise in the
fashion industry and its effect on social influencing have made outfit
compatibility a need. Thus, it necessitates an outfit compatibility model to
aid people in clothing recommendation. However, due to the highly subjective
nature of compatibility, it is necessary to account for personalization. Our
paper devises an attribute-wise interpretable compatibility scheme with
personal preference modelling which captures user-item interaction along with
general item-item interaction. Our work solves the problem of interpretability
in clothing matching by locating the discordant and harmonious attributes
between fashion items. Extensive experiment results on IQON3000, a publicly
available real-world dataset, verify the effectiveness of the proposed model.Comment: 10 pages, 5 figures, to be published in IEEE BigMM, 202
Neural Compatibility Modeling with Attentive Knowledge Distillation
Recently, the booming fashion sector and its huge potential benefits have
attracted tremendous attention from many research communities. In particular,
increasing research efforts have been dedicated to the complementary clothing
matching as matching clothes to make a suitable outfit has become a daily
headache for many people, especially those who do not have the sense of
aesthetics. Thanks to the remarkable success of neural networks in various
applications such as image classification and speech recognition, the
researchers are enabled to adopt the data-driven learning methods to analyze
fashion items. Nevertheless, existing studies overlook the rich valuable
knowledge (rules) accumulated in fashion domain, especially the rules regarding
clothing matching. Towards this end, in this work, we shed light on
complementary clothing matching by integrating the advanced deep neural
networks and the rich fashion domain knowledge. Considering that the rules can
be fuzzy and different rules may have different confidence levels to different
samples, we present a neural compatibility modeling scheme with attentive
knowledge distillation based on the teacher-student network scheme. Extensive
experiments on the real-world dataset show the superiority of our model over
several state-of-the-art baselines. Based upon the comparisons, we observe
certain fashion insights that add value to the fashion matching study. As a
byproduct, we released the codes, and involved parameters to benefit other
researchers
Fashion Meets Computer Vision: A Survey
Fashion is the way we present ourselves to the world and has become one of
the world's largest industries. Fashion, mainly conveyed by vision, has thus
attracted much attention from computer vision researchers in recent years.
Given the rapid development, this paper provides a comprehensive survey of more
than 200 major fashion-related works covering four main aspects for enabling
intelligent fashion: (1) Fashion detection includes landmark detection, fashion
parsing, and item retrieval, (2) Fashion analysis contains attribute
recognition, style learning, and popularity prediction, (3) Fashion synthesis
involves style transfer, pose transformation, and physical simulation, and (4)
Fashion recommendation comprises fashion compatibility, outfit matching, and
hairstyle suggestion. For each task, the benchmark datasets and the evaluation
protocols are summarized. Furthermore, we highlight promising directions for
future research.Comment: Accepted by ACM Computing Surveys (2021). 39 pages including 2 pages
of supplementary materials and 7 pages of referenc