Frozen Network Few-Shot Object Detection

In this paper, we propose a method that addresses two challenges faced by existing generalized few-shot object detection methods, limiting their scalability in practical applications: (i) the high training cost required for few-shot learning of a small number of target object samples, and (ii) the lack of variation in these samples. Leveraging the generalization ability of a deep neural network (DNN) trained with vision-language pretraining, we introduce a frozen network few-shot object detection framework in which a common DNN, shared with the pretraining phase, is used to model the appearance features of target objects without needing to be updated during few-shot learning. In experiments, we employed publicly available datasets with diverse object appearances for pretraining and few-shot learning. By comparing our proposed method’s detection accuracy with existing methods, we demonstrate that our method effectively addresses the challenges described above.