In recent years,significant progress has been made in the field of multimodal fusion research.Multimo-dal data provides a wealth of information compared to unimodal data.However,the category co-occurrence frequen-cy bias during multimodal fusion makes clothing compatibility prediction studies challenging.Therefore,we propose a multimodal correlation fashion clothing compatibility prediction model based on a hybrid graph neural network.The model deeply exploits the correlation between textual and visual modalities,and solves the problem of inaccu-rate compatibility prediction caused by the category co-occurrence frequency bias in the process of multimodal fu-sion by hybrid graph neural network,so as to improve the accuracy of clothing compatibility prediction.The model underwent experiments on the Polyvore Outfits and Polyvore Outfits-D open-source datasets for tasks related to fash-ion compatibility prediction and fill-in-the-blank.The results show that the model achieved AUC values of 0.928 and 0.878 for the dress compatibility task in the two datasets,and 62.41％ and 56.83％ accuracy for the fill-in-the-blank task,surpassing the performance of the baseline models used for comparison.
关键词
多模态/动态图神经网络/共现频率偏差/服饰兼容度
Key words
multimodality/dynamic graph neural network/co-occurrence frequency bias/clothing compatibility
维普
万方数据