Few-Shot Object Detection via Metric Learning

To achieve good results with the existing target detection framework, a large amount of annotated data is often needed. However, the acquisition of annotated data is a laborious process. It is even impossible to obtain sufficient annotated data in some categories. To reduce the dependence of deep learning model on large-scale data, a new end-to-end single-stage detector (FSSSD) network is proposed based on metric learning method. In this way, the objects that are not seen during training can be detected under the condition of providing a small number of samples. The main innovation of this paper is that the traditional single-stage detection model FCOS is improved and the Class Feature Extractor module is added to make it become the Correlation Detector. Thus, the model can extract the feature distribution of the support category from the small number of pictures provided by the support set. Thereafter, the model converts the query set feature map into the object probability distribution map, and fuses it with the original feature map to enhance the feature representation of the potential objects consistent with the supporting category on the feature map, so that the model pays more attention to the objects consistent with the supporting category in classification and regression. The method in this paper does not need fine-tuning or retraining at all when recognizing objects of a new category, and only needs to provide supporting pictures of the corresponding category during testing. At the same time, our modules are flexible and easy to migrate, theoretically suitable for all target detection models, and can improve the performance of these models on few-shot problems.