Multi-modal joint embedding for fashion product retrieval

Bandera, Antonio; Yu, Longlong; Simo-Serra, Edgar; Moreno-Noguer, Francesc

doi:10.1109/icip.2017.8296311

Cited by 17 publications

(8 citation statements)

References 22 publications

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“…The matched images can be recommended as design references. The retrieval input can be either text, images, or both [161][162][163][164].…”

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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“…The matched images can be recommended as design references. The retrieval input can be either text, images, or both [161][162][163][164].…”

Section: Spark Creative Inspirationmentioning

confidence: 99%

Big Data and AI-driven Product Design: A Survey

Quan

Zeng

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Designers and users put forward their requirements through images and text, search for related product images from databases or e-commerce websites, and the matched images will be recommended to designers and users as design references. The retrieval input can be text, images, or both of them [162,163,164,165,166]. For product, the input image provided by designers and users may be taken by their phone on the street or in a store, which is quite different from image databases and e-commerce websites in terms of shooting angle, condition, background, or posture [167,168,169,170,171].…”

Section: Product Design Based On Image Datamentioning

confidence: 99%

Big Data driven Product Design: A Survey

Quan¹,

Li²,

Zeng³

et al. 2021

Preprint

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With the improvement of living standards, user requirements of modern products are becoming increasingly more diversified and personalized. Traditional product design methods can no longer satisfy the market needs due to their strong subjectivity, small survey scope, poor real-time data, and lack of visual display, which calls for the development of big data driven product design methodology. Big data in the product lifecycle contains valuable information for guiding product design, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements; product images contain information of shape,color, and texture which can inspire designers to get initial design schemes more quickly or even directly generate new product images. How to efficiently collect product design related data and exploit them effectively during the whole product design process is thus critical to modern product design. This paper aims to conduct a comprehensive survey on big data driven product design. It will help researchers and practitioners to comprehend the latest development of relevant studies and applications centered on how big data can be processed, analyzed, and exploited in aiding product design. We first introduce several representative traditional product design methods and highlight their limitations. Then we discuss current and potential applications of textual data, image data, audio data, and video data in product design cycles. Finally, major deficiencies of existing data driven product design studies and future research directions are summarized. We believe that this study can draw increasing attention to modern data driven product design.Keywords product design • big data • Kansei engineering • QFD • generative design * Correspondence.

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“…Deep Metric Learning. Metric learning using deep models is a well-studied problem with many applications [3,25,28,34], especially where the output space is very large. Early approaches are based upon Siamese networks [7] with contrastive loss on pairwise data or relative triplet similarity comparisons [12,28].…”

Section: Related Workmentioning

confidence: 99%

Improving Calibration in Deep Metric Learning With Cross-Example Softmax

Veit¹,

Wilber²

2020

Preprint

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Modern image retrieval systems increasingly rely on the use of deep neural networks to learn embedding spaces in which distance encodes the relevance between a given query and image. In this setting, existing approaches tend to emphasize one of two properties. Triplet-based methods capture top-k relevancy, where all top-k scoring documents are assumed to be relevant to a given query Pairwise contrastive models capture threshold relevancy, where all documents scoring higher than some threshold are assumed to be relevant. In this paper, we propose Cross-Example Softmax which combines the properties of top-k and threshold relevancy. In each iteration, the proposed loss encourages all queries to be closer to their matching images than all queries are to all non-matching images. This leads to a globally more calibrated similarity metric and makes distance more interpretable as an absolute measure of relevance. We further introduce Cross-Example Negative Mining, in which each pair is compared to the hardest negative comparisons across the entire batch. Empirically, we show in a series of experiments on Conceptual Captions and Flickr30k, that the proposed method effectively improves global calibration and also retrieval performance.

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Multi-modal joint embedding for fashion product retrieval

Cited by 17 publications

References 22 publications

Big Data and AI-driven Product Design: A Survey

Big Data and AI-driven Product Design: A Survey

Big Data driven Product Design: A Survey

Improving Calibration in Deep Metric Learning With Cross-Example Softmax

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