Feature reconstruction and metric based network for few-shot object detection

In the object detection task, deep learning-based methods always need a large amount of annotated training data. However, annotating a large number of images is labor-intensive. In order to reduce the dependency of expensive annotations, we propose a novel end-to-end feature reconstruction and metric based network for few-shot object detection (FM-FSOD). FM-FSOD integrates metric learning and meta-learning to tackle the few-shot object detection task. FM-FSOD is a class-agnostic detection model that can accurately recognize novel categories without fine-tuning on novel categories. Specifically, to quickly learn the characteristics of novel categories, we propose a meta-representation module (MR module) to learn from intra-class mean prototypes and acquire the ability to reconstruct high-level features with the meta-learning method. To further conduct the similarity of features between support prototypes and query ROI features, we propose Pearson metric module (PR module), which serves as a classifier. Compared with the previous standard cosine distance module, the PR module enables the model to acquire robust ability for large bias features. We have conducted extensive experiments on benchmark datasets FSOD, MS COCO, and PASCAL VOC to demonstrate the feasibility and efficiency of our model. Comparing with the previous methods, FM-FSOD obtains comparable results.