Projection Modules for Few-Shot Object Detection

Abstract

In this paper, we propose a novel approach to enhance few-shot object detection (FSOD) by utilizing projection modules, which are commonly employed in self-supervised learning to improve feature transferability. We integrate a multilayer perceptron (MLP) as a projection module within a Faster RCNN model and design a tailored training strategy to facilitate the transfer of features from base to novel classes in FSOD. The MLP is used during the base training phase and removed when training on few-shot datasets to leverage more generalized intermediate features for novel classes. Our approach also incorporates affine layers before the classification and regression tasks, which perform element-wise scaling and bias adjustments on the input feature vectors. This effectively reweights the features, enhancing the separability of the feature space for their respective tasks while keeping the parameter count low to minimize overfitting. We evaluate our method against established FSOD techniques, such as Two-Stage Fine-Tuning and Decoupled Faster RCNN, using standard benchmark datasets Pascal VOC and MS COCO. Our results demonstrate significant performance improvements, especially in scenarios with very few training examples.