特征互斥化的目标检测域适应方法OA北大核心CSTPCD

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.