Few-shot learning, which aims to identify new classes with few samples, is an increasingly popular and crucial research topic in the machine learning . Recently, the development of deep learning has deepened the network structure of a few-shot model, thereby obtaining deeper features from the samples. This trend led to an increasing number of few-shot learning models pursuing more complex structures and deeper features. However, discarding shallow features and blindly pursuing the depth of sample feature levels is not reasonable. The features at different levels of the sample have different information and characteristics. In this paper, we propose a few-shot image classification model based on deep and shallow feature fusion and a coarse-grained relationship score network (HFFCR). First, we utilize networks with different depth structures as feature extractors and then fuse the two kinds of sample features. The fused sample features collect sample information at different levels. Second, we condense the fused features into a coarse-grained prototype point. Prototype points can better represent the information in this class and improve classification efficiency. Finally, we construct a relationship score network, concatenating the prototype points and query samples into a feature map and sending it into the network to calculate the relationship score. The classification criteria for learnable relationship scores reflect the information difference between the two samples. Experiments on three datasets show that HFFCR has advanced performance.

Abstract State‐of‐the‐art object detection models rely on large‐scale datasets for training to achieve good precision. Without sufficient samples, the model can suffer from severe overfitting. Current explorations in few‐shot object detection are mainly divided into meta‐learning‐based methods and fine‐tuning‐based methods. However, existing models do not focus on how feature maps should be processed to present more accurate regions of interest (RoIs), leading to many non‐supporting RoIs. These non‐supporting RoIs can increase the burden of subsequent classification and even lead to misclassification. Additionally, catastrophic forgetting is inevitable in both few‐shot object detection models. Many models classify directly in low‐dimensional spaces due to insufficient resources, but this transformation of the data space can confuse some categories and lead to misclassification. To address these problems, the Feature Reconstruction Detector (FRDet) is proposed, a simple yet effective fine‐tune‐based approach for few‐shot object detection. FRDet includes a region proposal network (RPN) based on channel attention and space attention called Multi‐Attention RPN (MARPN) and a head based on feature reconstruction called Feature Reconstruction Head (FRHead). MARPN utilizes channel attention to suppress non‐supporting classes and spatial attention to enhance support classes based on Attention RPN, resulting in fewer but more accurate RoIs. Meanwhile, FRHead utilizes support features to reconstruct query RoI features through a closed‐form solution, allowing for a comprehensive and fine‐grained comparison. The model was validated on the PASCAL VOC, MS COCO, FSOD, and CUB200 datasets and achieved better results.