Few-shot semantic segmentation based on inter-set semantic complementarity of two levels
Existing few-shot semantic segmentation models typically rely on single level semantic feature,but due to the small sample data of less sample size and different attributes of semantic features at each level,it is difficult for the network model to extract semantic features at a single level to ensure the segmentation ability and generalization.To solve this problem,a few-shot semantic segmentation based on inter-set semantic complementarity of two levels is proposed.In this method,the high-level semantic features of the support set with strong category are used to weight the generalization middle-level semantic features of the query set,and the generalization capability of the query set semantic features is preserved while the features of the query set target categories are enhanced.In addition,the model enhances the interaction between the two sets of information by optimizing support middle-level semantic feature and constructing non-parametric learning prior information for the query set,so as to obtain richer discriminant information.Experimental results based on PASCAL-5i data set show that the proposed method is effective in solving the problem of few-shot semantic segmentation.The mIoU value of the network can reach 44.6%and 48.8%in 1-shot and 5-shot settings,respectively,and its results surpass some state-of-the-art method.and the number of parameters in the network model is controlled within the acceptable range.
few-shot semantic segmentationprototype learninginter-set semantic features of two levelsmulti-scale
国家科技期刊平台
NSTL
万方数据
维普
