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In the object detection task, CNN (Convolutional neural networks) modelsalways need a large amount of annotated examples in the training process. Toreduce the dependency of expensive annotations, few-shot object detection hasbecome an increasing research focus. In this paper, we present an effectiveobject detection framework (MM-FSOD) that integrates metric learning andmeta-learning to tackle the few-shot object detection task. Our model is aclass-agnostic detection model that can accurately recognize new categories,which are not appearing in training samples. Specifically, to fast learn thefeatures of new categories without a fine-tuning process, we propose ameta-representation module (MR module) to learn intra-class mean prototypes. MRmodule is trained with a meta-learning method to obtain the ability toreconstruct high-level features. To further conduct similarity of featuresbetween support prototype with query RoIs features, we propose a Pearson metricmodule (PR module) which serves as a classifier. Compared to the previouscommonly used metric method, cosine distance metric. PR module enables themodel to align features into discriminative embedding space. We conductextensive experiments on benchmark datasets FSOD, MS COCO, and PASCAL VOC todemonstrate the feasibility and efficiency of our model. Comparing with theprevious method, MM-FSOD achieves state-of-the-art (SOTA) results.

MM-FSOD: Meta and metric integrated few-shot object detection