计算机工程2026，Vol.52Issue(3)：141-151,11.DOI:10.19678/j.issn.1000-3428.0070093
基于查询引导和语义增强的小样本目标检测方法
Few-shot Object Detection Method Based on Query Guidance and Semantic Enhancement
摘要
Abstract
This study proposes a Few-Shot Object Detection(FSOD)method based on a query-guided strategy and semantic enhancement mechanism to address the following concerns:the lack of prototypical key information,insufficient adaptation to query images in the meta-learning paradigm,and the detector's sensitivity to the variance of the novel class leading to misclassification.The Query Guidance Module(QGM)conditionally couples query-aware information into support features by learning the correlation between the query and support features,aiming to generate specific and representative prototypes for each query image.The Visual Semantic Enhancement Module(VSEM)distils the knowledge from textual semantic information that matches the novel class of visual features and adaptively enhances these features to improve their discriminability and mitigate variance sensitivity for better classification.In addition,the classification and regression tasks are decoupled,and semantic enhancement is performed on the classification branch to facilitate the model's understanding of the target semantics.The experimental results demonstrate that,compared to the currently known state-of-the-art SMPCCNet method,the proposed approach achieves an average improvement of 2.2 percentage points in novel Average Precision(nAP)on the PASCAL VOC dataset and an average improvement of 1.0 percentage points in Average Precision(AP)on the MS COCO dataset,validating its effectiveness.关键词
目标检测/小样本学习/元学习/查询引导原型/语义增强Key words
object detection/few-shot learning/meta learning/query-guided prototype/semantic enhancement分类
信息技术与安全科学引用本文复制引用
谢斌红,石宇飞,张睿,张英俊..基于查询引导和语义增强的小样本目标检测方法[J].计算机工程,2026,52(3):141-151,11.基金项目
山西省基础研究计划面上项目(20210302123216) （20210302123216）
吕梁市引进高层次科技人才重点研发项目(2022RC08) （2022RC08）
山西省产教融合研究生联合培养示范基地项目(2022JD11). （2022JD11）
