首页|Researchers from University of Electronic Science and Technology of China Discus s Findings in Computational Intelligence (Hierarchical Multimodal Graph Learning for Outfit Compatibility Modelling)
Researchers from University of Electronic Science and Technology of China Discus s Findings in Computational Intelligence (Hierarchical Multimodal Graph Learning for Outfit Compatibility Modelling)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning - Computational Intelligence. According to news reporting origina ting in Shenzhen, People’s Republic of China, by NewsRx journalists, research st ated, “Outfit compatibility modelling plays a significant role in e-commerce dec ision-making, but the existing methods are restricted to modelling the visual an d textual information and have neglected the direct contribution of category lab els and the differences in semantic richness among different modalities. This pa per addresses these issues by developing a hierarchical multimodal graph learnin g framework for outfit compatibility modelling called HMGL-OCM, which consists o f an item-level graph network and a modality-level graph network.”
ShenzhenPeople’s Republic of ChinaAs iaComputational IntelligenceMachine LearningUniversity of Electronic Scien ce and Technology of China
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