Research And Implementation On Meta-learning Few-shot Object Detection System

Few-shot Object Detection(FSOD)has achieved promising results in recent years,which has become a research hot spot.A lot of recent works are designed based on meta learning paradigm,as the model can get a more generalized representation.However,there still exists two main issues in these approaches:For one,these models express ’overconfident’ to out-of-distribution sample,which severely affects the stability of vision system in real-world application.For another,the generalization of the model is not promising enough,which indicates the performance of the model is still under the bar of application.Furthermore,mainstream deep learning service platforms rarely provide few-shot object detection related services,especially in terms of out-of-distribution detection testing for model stability and generalization testing evaluation for model availability.Therefore,focusing on solving the above two technical issues and one industrial demand,this paper has three-folds contribution:(1)This paper proposes and implements a meta-learning out-of-distribution detection technique based on feature space transformation.The technique uses multi-modal semantic embedding and fusion,and constructs an adaptive space transformation module through an adaptive mechanism to bridge the gap between meta-learning methods and traditional out-of-distribution detection techniques,thus enhancing the perception ability of models.for out-of-distribution samples.(2)This paper proposes and implements a meta-learning-based multi-view few-shot object detection method.The method uses multi-view generation and fusion techniques to design a highly generalizable meta-learning representation method to improve the classification performance of models.It also designs a feature enhancement module based on cross-attention to increase task-specific features of the query sample and improve the detection performance of model.The ultimate goal is to reduce false alarms and missed detections.(3)This article proposes and implements a platform focuing on developing few-shot object detection algorithms,which provides a complete engineering solution for building the platform.The platform provides data services,model services,few-shot capability testing services,and out-of-distribution detection capability testing services,making it easy for developers to accurately evaluate their models.