中南民族大学学报（自然科学版）2024，Vol.43Issue(6)：772-780,9.DOI:10.20056/j.cnki.ZNMDZK.20240607
基于多尺度四维特征融合的小样本语义分割
Few-shot semantic segmentation based on multiscale 4D feature fusion
摘要
Abstract
Abatract The existing semantic segmentation method relies on sufficient pixel-level image labeling,while the segmentation model needs to be trained under the new sample conditions,which brings the problem of manually labeling images.Therefore,few-shot semantic segmentation method is proposed to solve such problems.The current few-shot segmentation method mainly adopts the prototype learning method,while the prototype learning method lacks pixel-level support-level features to guide query image segmentation,resulting in the problem of low segmentation accuracy.Based on this,a four-dimensional feature fusion and attention-enhancing few-shot segmentation network is designed.In order to obtain the pixel-level representation information of rich support set features for query images,four-dimensional convolution is used in the feature pyramid structure to gradually compress advanced semantic features and intermediate semantic features into super-correlated features,which is then used to segment the query image.At the same time,the test results of mIoU in the PASCAL-5i dataset 1-shot setting were improved by 0.6%and 2.3%compared to the HSNet and PFENet,respectively.关键词
小样本语义分割/多尺度特征/超相关特征/交叉注意力Key words
few-shot semantic segmentation/multi-scale features/hypercorrelation features/cross-attention分类
信息技术与安全科学引用本文复制引用
丁月,陈少波,尹作轩..基于多尺度四维特征融合的小样本语义分割[J].中南民族大学学报（自然科学版）,2024,43(6):772-780,9.基金项目
中央高校基本科研业务费专项资金资助项目(CZY22012) （CZY22012）
