GLOSS: Global-Local Matching Network Towards Outfit Recommendation for Diverse Body Shapes and Scenes

The evolution of individuals' living standards has transformed clothing preferences, elevating fashion beyond mere utility to a potent means of self-expression. However, the intricate task of outfit selection persists as a challenge, marked by traditional methods facing challenges such as the oversight of combined factors of scene and body shape, insufficient emphasis on detail-oriented matching, and overreliance on rigid hierarchical structures. To tackle these challenges, this article introduces a novel model, termed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b>lobal-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b>ocal matching network towards <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b>utfit recommendation for diverse body <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b>hapes and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b>cenes (GLOSS). Specifically, we first introduce a newly compiled fashion dataset, StreetFashion, to capture the combined factors of body shapes and scene characteristics. Additionally, we develop innovative multi-level globality- and locality-aware matching methods to enhance the accuracy of outfit recommendations by comprehensively considering both global and local relationships among clothing items, outfits, users, and scenes. Furthermore, we develop a personalized outfit heterogeneous graph that incorporates historical interactions among fashion entities, enabling effective modeling of nonstrict hierarchical relationships. Evaluation conducted on both our collected dataset and an adapted existing dataset demonstrates the effectiveness of our proposed approach in outfit recommendation. Our codes are released at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ChenrMa/GLOSS</uri>.

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Publication Details

Journal
IEEE Transactions on Multimedia
Published
2025-01-01
DOI
https://doi.org/10.1109/tmm.2025.3613163
Primary Topic
3D Shape Modeling and Analysis
Type
article
Field-Weighted Citation Impact
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article

GLOSS: Global-Local Matching Network Towards Outfit Recommendation for Diverse Body Shapes and Scenes

Cong Ma, Yuxin Ding, Jianghong Ma, Zhao Zhang et al.
IEEE Transactions on Multimedia
3D Shape Modeling and Analysis
article

GLOSS: Global-Local Matching Network Towards Outfit Recommendation for Diverse Body Shapes and Scenes

Cong Ma, Yuxin Ding, Jianghong Ma, Zhao Zhang, Haijun Zhang, Huiyue Sun
article en

Abstract

The evolution of individuals' living standards has transformed clothing preferences, elevating fashion beyond mere utility to a potent means of self-expression. However, the intricate task of outfit selection persists as a challenge, marked by traditional methods facing challenges such as the oversight of combined factors of scene and body shape, insufficient emphasis on detail-oriented matching, and overreliance on rigid hierarchical structures. To tackle these challenges, this article introduces a novel model, termed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b>lobal-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b>ocal matching network towards <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b>utfit recommendation for diverse body <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b>hapes and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b>cenes (GLOSS). Specifically, we first introduce a newly compiled fashion dataset, StreetFashion, to capture the combined factors of body shapes and scene characteristics. Additionally, we develop innovative multi-level globality- and locality-aware matching methods to enhance the accuracy of outfit recommendations by comprehensively considering both global and local relationships among clothing items, outfits, users, and scenes. Furthermore, we develop a personalized outfit heterogeneous graph that incorporates historical interactions among fashion entities, enabling effective modeling of nonstrict hierarchical relationships. Evaluation conducted on both our collected dataset and an adapted existing dataset demonstrates the effectiveness of our proposed approach in outfit recommendation. Our codes are released at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ChenrMa/GLOSS</uri>.

IEEE Transactions on MultimediaVol. 27
Hefei University of Technology (CN), Harbin Institute of Technology (CN)
Openalex Percentile: Top 72%
3D Shape Modeling and Analysis
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