Creating Capsule Wardrobes from Fashion Images

We propose to automatically create capsule wardrobes. Given an inventory of candidate garments and accessories, the algorithm must assemble a minimal set of items that provides maximal mix-and-match outfits. We pose the task as a subset selection problem. To permit efficient subset selection over the space of all outfit combinations, we develop submodular objective functions capturing the key ingredients of visual compatibility, versatility, and user-specific preference. Since adding garments to a capsule only expands its possible outfits, we devise an iterative approach to allow near-optimal submodular function maximization. Finally, we present an unsupervised approach to learn visual compatibility from "in the wild" full body outfit photos; the compatibility metric translates well to cleaner catalog photos and improves over existing methods. Our results on thousands of pieces from popular fashion websites show that automatic capsule creation has potential to mimic skilled fashionistas in assembling flexible wardrobes, while being significantly more scalable.Comment: Accepted to CVPR 201

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

Capsule wardrobe based on the slow fashion principles: a way to increase sustainability in fashion

O slow fashion busca desacelerar a produção industrial em massa de produtos de moda ao sobrepor a valorização da qualidade, bem como das pessoas que trabalham em toda a cadeia produtiva, em detrimento à quantidade e à exploração dos recursos. O armário-cápsula é uma metodologia utilizada em forma de desafio para propor que as pessoas usem por um determinado período de tempo um modelo quantitativo e pré-delimitado de roupas, combinando-as, sem consumir novos produtos. Assim, objetivo desse trabalho é propor um novo método de armário-cápsula que traga as informações do slow fashion aliada aos princípios utilizados pelos consultores de imagem na construção de armários-cápsula. Foi executada uma pesquisa bibliográfica e documental, exploratória e qualitativa. Como resultado, foi possível obter um produto gráfico em forma de mapa mental que possibilita propiciar aos consumidores de moda a construção guiada de um armário-cápsula, interligando seu usuário a uma moda consciente e sustentável.Slow fashion seeks to decelerate the mass industrial production of fashion products by overriding the valorization of quality, as well as the people who work throughout the production chain, to the detriment of the quantity and exploitation of resources. The capsule wardrobe is a methodology used in the form of a challenge to propose that people use for a certain period of time a quantitative and pre-defined model of clothes, combining them, without consuming new products. Thus, the objective of this work is to propose a new method of capsule wardrobe that brings the information of slow fashion combined with the principles used by image consultants in the construction of capsule cabinets. A bibliographical and documentary, exploratory and qualitative research was carried out. As a result, it was possible to obtain a graphic product in the form of a mind map that makes it possible to provide fashion consumers with the guided construction of a capsule cabinet, connecting its user to conscious and sustainable fashion

Utilizing social media to inform inclusive apparel design for baby boomer women

Apparel design solutions for an aging population with unique needs are lacking in the retail industry and specifically for baby boomer women. The fashion industry does not acknowledge that older women value fashion and are interested in maintaining a sense of style into old age. This study focused on expanding inclusive apparel design strategies and specifically to evaluate baby boomer women’s clothing preferences through their fashion blogs. Vital to this research was the design and construction of a mini-collection of clothing that served two purposes. First, as a starting point in the development of apparel prototypes designed specifically to satisfy multiple needs without calling attention to special requirements. Second, to provide physical garments that users interacted with and provided feedback about to inform recommendations for inclusive apparel prototypes designed for baby boomer women. The results from this study found that the study participants were interested in a variety of clothing styles and silhouettes and specifically in fashion trends that could be modified to accommodate an aging body. Design solutions addressed a range of needs without calling attention to disabilities. Simple designs with easy to manage closures and special details to enhance personal style were favored by the study participants. This research contributed to apparel design scholarship, fully documented and evaluated the inclusive design process and added to the body of qualitative research that utilizes social media and specifically blogs as a rich data source

Joint Session-Item Encoding for Session-Based Recommendation: A Metric- Learning Approach with Temporal Smoothing

In recommendation systems, a system is in charge of providing relevant recommendations towards users with either a clear target in mind or a mere vague mental representation. Session-based recommendation targets a specific scenario in recommendation systems, where users are anonymous. Thus the recommendation system must work under more challenging conditions, having only the current session to extract any user preferences to provide recommendations. This setting requires a model capable of understanding and relating different inter- actions across different sessions involving different items. This dissertation reflects such relationships on a commonly learned space for sessions and items. Such space is built using metric-learning, which can capture such relationships and build such space, where the distances between the elements (session and item embeddings) reflect how they relate to each other. We then use this learned space as the intermediary to provide relevant rec- ommendations. This work continues and extends on top of other relevant work showing the potential of metric-learning addressed to the session-based recommendation field. This dissertation proposes three significant contributions: (i) propose a novel joint session-item encoding model with temporal smoothing, with fewer parameters and the inclusion of temporal characteristics in learning (temporal proximity and temporal re- cency); (ii) enhanced recommendation performance surpassing other state-of-the-art metric-learning models for session-based recommendation; (iii) a thorough critical analy- sis, addressing and raising awareness to common problems in the field of session-based recommendation, discussing the reasons behind them and their impact on model perfor- mance.Em sistemas de recomendação, um sistema fica encarregue de fornecer recomendações relevantes aos seus utilizadores que podem ter, ou uma ideia concreta daquilo que pre- tendem ou apenas uma vaga representação mental. Recomendação com base na sessão dirige-se principalmente a um cenário específico de sistemas de recomendação, onde os utilizadores são anónimos. Ou seja, estes sistemas têm de ser capazes de funcionar em condições mais desfavoráveis, tendo apenas a sessão atual disponível como input do utilizador para efetuar recomendações. Este contexto requer um modelo capaz de perceber e relacionar diferentes interações ao longo de várias outras sessões envolvendo diferentes itens. Esta dissertação reflete tais interações por via de um espaço comum, que é aprendido, para representar sessões e itens. Este espaço é construído usando metric-learning, técnica que consegue capturar tais relações e construir o espaço em questão, no qual a distância entre os vários elementos (embeddings de sessões e itens) reflete como estes se relacionam entre si. Usamos este espaço, que foi aprendido, como intermediário no fornecimento de recomendações rele- vantes. Este trabalho continua e extende para além de outros trabalhos relevantes na área que mostraram o potencial de aplicar metric-learning para o domínio de recomendação com base na sessão. Esta dissertação propõe as seguintes três principais e significativas contribuições: (i) propõe um novo modelo de codificação sessão-item conjunto com suavização temporal, com menos parâmetros e com a inclusão de características temporais no processo de aprendizagem (proximidade temporal e recência); (ii) um desempenho de recomenda- ção melhorado que ultrapassa outros métodos do estado-da-arte que utilizam técnicas de metric-learning para sistemas de recomendação com base na sessão; (iii) uma análise cuidada, que foca e tenta destacar alguns erros comuns neste campo de sistemas de re- comendação com base na sessão, discutindo as razões por detrás de tais erros e o seu impacto no desempenho dos modelos

PROCEEDINGS 5th PLATE Conference

The 5th international PLATE conference (Product Lifetimes and the Environment) addressed product lifetimes in the context of sustainability. The PLATE conference, which has been running since 2015, has successfully been able to establish a solid network of researchers around its core theme. The topic has come to the forefront of current (political, scientific & societal) debates due to its interconnectedness with a number of recent prominent movements, such as the circular economy, eco-design and collaborative consumption. For the 2023 edition of the conference, we encouraged researchers to propose how to extend, widen or critically re-construct thematic sessions for the PLATE conference, and the paper call was constructed based on these proposals. In this 5th PLATE conference, we had 171 paper presentations and 238 participants from 14 different countries. Beside of paper sessions we organized workshops and REPAIR exhibitions

Material memorialisation: new narratives from old

I am interested in the designer as collector. A hoarder of artifacts and information from the past, which teaches us history, to inspire future work. This project involves research into the techniques and concepts behind Make Do and Mend of the WWII era (1939-1945) and subsequent rationing period. Referencing mid-twentieth century garments and Make Do and Mend strategies for preservation, conservation, recycling and economy of materials collapses the distinction between the past and present. The vintage garment has its own history that becomes a quality or attribute of the garment. It is a unique and highly valued artifact of the past. We can use vintage clothing as a means of making ourselves, our practice and our place in the design world knowable. The garment is a window through which the past might be understood; especially past ways of making and the value inherent in traditional skills. Nostalgia is a psychological lens through which we construct, maintain and reconstruct our identity as fashion designers. This project explores nostalgia as a critical framework and how it may inform contemporary and future design practice

Sustainability in design: now! Challenges and opportunities for design research, education and practice in the XXI century

Copyright @ 2010 Greenleaf PublicationsLeNS project funded by the Asia Link Programme, EuropeAid, European Commission