A multimodal multi-label classification method based on hypergraph
Label classification aims to select the most relevant subset of labels from a set of labels to tag an instance,which has become a hot issue in the field of artificial intelligence.Traditional multi-label learning methods mainly focus on identifying single-modal data,with limited research on mining high-order correlation between multi-modal data.To address the issue of insufficient representation of high-order correlations between multi-modal data in multi-label scenarios,this paper proposed a multi-modal multi-label classification method based on hypergraphs.The hypergraph model is introduced to model the high-order correlations of multi-modal data,and the fusion of multi-modal features and hyper-edge convolution operation are utilized to achieve the mining of multi-modal data relationships and fea-ture recognition,thus improving the performance of multi-modal multi-label classification.Experiments were conducted on the movie genre classification task,and the proposed method was compared with tra-ditional methods.The experimental results show that the proposed method outperforms the comparison methods in terms of accuracy,precision,and F1 score,demonstrating the effectiveness of the method.
万方数据
维普
