计算机科学与探索2026，Vol.20Issue(9)：2678-2689,12.DOI:10.3778/j.issn.1673-9418.2512001
基于类原型网络及动态语义注意力的小样本语义边缘检测
Class-Prototype Network and Dynamic Semantic Attention Module for Few-Shot Semantic Edge Detection
摘要
Abstract
Accurate semantic edge information is crucial for machine vision applications such as object reconstruction,image generation,and medical imaging.However,existing deep learning-based semantic edge detection methods require a large amount of annotated data to train the network model,and the cost of obtaining a sufficient number of labeled samples is prohibitively high.To overcome this limitation,this paper proposes a few-shot semantic edge detection method based on class-prototype network and dynamic semantic attention,named DSAPNet(dynamic semantic attention and prototype network).It employs a meta-learning training strategy to locate semantic edges of novel categories using only a few annotated samples.The proposed network incorporates a class-prototype-based small-scale semantic segmenter,which provides multi-scale semantic information to the semantic edge detector at different stages of the network architecture.A dynamic semantic attention module is designed,which generates multi-scale spatial attention maps to highlight semantically meaningful regions and focuses the decoder module on those regions,effectively supplementing missing semantic information at the edges.A receptive field enhancement block and a context information fusion module are constructed,improving the receptive field of the output features in the encoding path and integrating their contextual information.Experiments on two few-shot semantic edge detection datasets,i.e.,FSE-1000 and SBD-5i,reveal the effectiveness of the proposed DSAPNet in the N-way K-shot few-shot semantic edge detection task.关键词
元学习/小样本学习/语义边缘检测/类原型网络Key words
meta-learning/few-shot learning/semantic edge detection/class-prototype network分类
信息技术与安全科学引用本文复制引用
鲍士水,高歌,葛斌,许光宇..基于类原型网络及动态语义注意力的小样本语义边缘检测[J].计算机科学与探索,2026,20(9):2678-2689,12.基金项目
安徽理工大学引进人才科研启动基金(2024yjrc38) （2024yjrc38）
国家自然科学基金(61471004). This work was supported by the Scientific Research Launch Fund for Introduced Talents of Anhui University of Science and Technology(2024yjrc38),and the National Natural Science Foundation of China(61471004). （61471004）
