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