HAPE:Hybrid Attention and Prototype Extracting for Few-Shot Object Detection

Few-shot object detection aims to identify objects from limited annotated samples. It has received extensive attention in recent years and has made great progress. Attention methods currently applied to the few-shot problem often ignore the interaction between features of different dimensions. This will cause the model uable to pay enough attention to important areas., thus affecting the detection accuracy. In addition, existing methods for obtaining class prototypes usually rely on simple global average pooling, which cannot make the prototype contain perceptual information and is not representative enough, resulting in the generated class prototype not accurately reflecting the characteristics of the target class. To address these problems, our method is based on a two-stage fine-tuning paradigm, studies the dimensional interaction of features, and proposes a Hybrid Attention network to acquire attention weights by obtaining the interaction between different feature dimensions. To make the prototype features contain more representative perceptual information, we propose a Prototype Fusion Network to allow the information of the query image to participate in the generation process of the prototype features. Our method has achieved competitive improvements based on the baseline method, and its performance has been verified by extensive experiments on the general datasets PASCAL VOC and MS COCO. For the general dataset PASCAL VOC, our method improves the AP50 on split 1 by 9.3% on 1-shot and 7.7% on 3-shot. Index Terms─