Abstract: Abstract: At present， it is difficult to obtain the data of some industry application scenarios， and the problem of few shot has become an important factor restricting the application and promotion of deep learning technology. In this paper， few shot method is adopted to improve the performance of the model in the absence of data and reduce the dependence of the deep learning model on data， and few-shot object detection via learnable memory feature pyramid network is proposed to retain cleaner multi-scale feature information for classifier prediction. With the help of the adaptive feature fusion module， the network can choose the emphasis ratio among the features of different levels to maximize the retention of discriminant feature information of different scales. At the same time， we also add a retrospective feature alignment module to alleviate the feature confusion effect introduced by stacking feature layers. The experimental results show that the model performance can be effectively improved by overcoming the dependence on data， and the improved model can surpass other existing models of the same type in the COCO dataset and VOC dataset. In particular， when the prior parameter k is set to 5 in VOC dataset， nAP50 increases by 4.8 to 44.7； when the prior parameter k is set to 30 in COCO dataset， nAP50 increases by 4.0 to 29.4.

Key words: Key words： few shot, adaptive fusion, feature alignment, feature pyramid network