Learning Style Compatibility for Furniture

Aggarwal, Divyansh; Valiyev, Elchin; Sener, Fadime; Yao, Angela

doi:10.1007/978-3-030-12939-2_38

Cited by 9 publications

(15 citation statements)

References 31 publications

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“…Finally, the proposed VICTOR and FLIP fine-tuning are not limited to applications within the Fashion domain. Future works could experiment with other visually-driven domains such as exterior and interior architecture design [32].…”

Section: Discussionmentioning

confidence: 99%

VICTOR: Visual incompatibility detection with transformers and fashion-specific contrastive pre-training

Papadopoulos

Koutlis

Papadopoulos

et al. 2023

Journal of Visual Communication and Image Representation

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Section: Discussionmentioning

confidence: 99%

VICTOR: Visual incompatibility detection with transformers and fashion-specific contrastive pre-training

Papadopoulos

Koutlis

Papadopoulos

et al. 2023

Journal of Visual Communication and Image Representation

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“…Bell et al [BB15] inferred furniture compatibility based on visual similarity, trained siamese network models using contrast loss, and applied them to furniture retrieval. Aggarwal et al [AVSY18] trained a siamese network based on binary groups (A, B, Y ), where A and B represent two pieces of furniture, respectively; Y ∈ {0, 1} is the compatible label, where Y = 1 represents a positive compatible pair and Y = 0 represents a negative one. They trained a CNN model to learn furniture style embeddings by applying the following contrast loss:…”

Section: Binary Group-based Methodsmentioning

confidence: 99%

A Survey of Personalized Interior Design

Wang

Liang

Huai

et al. 2023

Computer Graphics Forum

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Interior design is the core step of interior decoration, and it determines the overall layout and style of furniture. Traditional interior design is usually laborious and time‐consuming work carried out by professional designers and cannot always meet clients' personalized requirements. With the development of computer graphics, computer vision and machine learning, computer scientists have carried out much fruitful research work in computer‐aided personalized interior design (PID). In general, personalization research in interior design mainly focuses on furniture selection and floor plan preparation. In terms of the former, personalized furniture selection is achieved by selecting furniture that matches the resident's preference and style, while the latter allows the resident to personalize their floor plan design and planning. Finally, the automatic furniture layout task generates a stylistically matched and functionally complete furniture layout result based on the selected furniture and prepared floor plan. Therefore, the main challenge for PID is meeting residents' personalized requirements in terms of both furniture and floor plans. This paper answers the above question by reviewing recent progress in five separate but correlated areas, including furniture style analysis, furniture compatibility prediction, floor plan design, floor plan analysis and automatic furniture layout. For each topic, we review representative methods and compare and discuss their strengths and shortcomings. In addition, we collect and summarize public datasets related to PID and finally discuss its future research directions.

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“…Most products do not stand alone, and their compatibility with other products must be considered during the design process. To address this, Aggarwal et al [181] used Siamese networks to assess the style compatibility between pairs of furniture images. They also proposed a joint visual-text embedding model for recommendation, based on furniture type, color, and material.…”

Section: Spark Creative Inspirationmentioning

confidence: 99%

Big Data and AI-driven Product Design: A Survey

Quan

Zeng

et al. 2023

Preprint

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As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI driven product design methodology with significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review on big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited in aiding product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design.

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Learning Style Compatibility for Furniture

Cited by 9 publications

References 31 publications

VICTOR: Visual incompatibility detection with transformers and fashion-specific contrastive pre-training

VICTOR: Visual incompatibility detection with transformers and fashion-specific contrastive pre-training

A Survey of Personalized Interior Design

Big Data and AI-driven Product Design: A Survey

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