计算机工程与应用2024，Vol.60Issue(10)：198-208,11.DOI:10.3778/j.issn.1002-8331.2301-0103
特征互斥化的目标检测域适应方法
Domain Adaptive Object Detection Method Based on Feature Mutual Exclusion
摘要
Abstract
Recently,distillation learning has become a common technical means in the field of unsupervised object detec-tion domain adaptation.However,due to the feature shift of distillation,the accuracy of the pseudo-labels obtained on the target domain is not so accurate,which has a certain negative impact on the target domain precise detection.Therefore,a feature mutual exclusion method is proposed,including feature distribution mutual exclusion and feature attribute mutual exclusion.The feature distribution mutual exclusion is used to prompt the feature distribution of different categories to be mutually exclusive,while the feature attribute mutual exclusion realizes that the classifiers mainly rely on mutual exclu-sive attributes when classifying different categories of features.In addition,a strong-weak augment consistency method is proposed to constrain the consistency of the network prediction,so that the features extracted by the network will mainly contain attributes related to the target domain detection,thereby improving the effect of the feature mutual exclusion method.Extensive experiments are conducted on several domain adaptation scenarios.The results show the effectiveness of the proposed method compared with other state-of-the-art methods under the same experimental settings.关键词
目标检测/无监督域适应/蒸馏学习/计算机视觉Key words
object detection/unsupervised domain adaptation/distillation learning/computer vision分类
信息技术与安全科学引用本文复制引用
李润泽,王子磊..特征互斥化的目标检测域适应方法[J].计算机工程与应用,2024,60(10):198-208,11.基金项目
国家自然科学基金重点项目(61836008). （61836008）
