Few Shot Object Detection via a Generalized Feature Extraction Net

Abstract

It is a new problem for deep learning to train a model on a small number of known targets to detect this object. Many recent studies are based on fine-tuning methods to solve. However, there is a lot of redundant information in the model during feature extraction, which will aggravate the difficulty of fine-tuning the model. Moreover, the neural network using the cross-entropy loss function classifier trained in few shots is prone to overfitting. We use the RS structure to reduce the number of channels in the model to reduce the repeated features in feature extraction. In addition, we use the Pearson distance function to calculate the classification loss of the model, to use the nonparametric method to reduce the number of parameters and prevent overfitting. Experimental results show that our method is better than the previous methods on Pascal VOC and FSOD datasets.