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Oct 24, 2020 · Therefore, a multi-modal input graph neural network (MI-GNN) model is proposed to solve these shortcomings. This model can not only better ...
Technology changes life. Recently, state-of-the-art of deep learning has inspired the development of deep-learning based approaches for outfit compatibility ...
Multi-Modal Input Mode via Graph Neural Networks for Outfit Compatibility. Huaiguang Wu, Yan Li, Baohua Jin, Wenjun Shi, and Bin Lu. Int J Performability Eng ...
Multi-Modal Input Mode via Graph Neural Networks for Outfit Compatibility. H. Wu, Y. Li, B. Jin, W. Shi, and B. Lu. Int. J. Perform. Eng., 17 (1): 50-59 (2021 ).
Apr 28, 2024 · The objective of this paper is to explore the use of different graph-based frameworks for the representation of clothing/accessory items and outfits in the ...
In this project, we follow two existing approaches that employ graphs to represent outfits and use modified versions of the Graph neural network (GNN) ...
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Abstract—Outfit compatibility modeling, which aims to au- tomatically evaluate the matching degree of an outfit, has drawn great research attention.
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Feb 21, 2019 · multi-modal data as input, which can also predict the compatibility scores of outfits. This method focus on multi-modal information. GGNN ...
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Mar 6, 2024 · Outfit compatibility modeling, which aims to automatically evaluate the matching degree of an outfit, has drawn great research attention.
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Moving one step forward, recent studies organize the outfit as an item graph and employ graph neural networks to fulfil the compatibility modeling task.
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