Multifeature fusion embedding network for unbiased scene graph generation

Scene Graph Generation (SGG) aims to bridge images and text. Addressing the issues of data distribution imbalance and long-tail effects in existing SGG methods, we propose a new Multi-feature Fusion Embedding Network(MFE-Net). This network optimizes relationship prediction through multi-level visual feature fusion and hard-constrained semantic feature classification, aiming to reduce inter-class confusion and strengthen intra-class consistency. Experiments on the Visual Genome dataset demonstrate that our model significantly enhances the accuracy of relationship recognition, achieving a new level of performance. The research not only optimizes SGG performance but also provides new strategies for addressing classification problems involving easily confused relationships.