Hallucination Improves Few-Shot Object Detection

Abstract

Learning to detect novel objects with a few instances is challenging. A particularly challenging but practical regime is the extremely-low-shot regime (less than three training examples). One critical factor in improving few-shot detection is to handle the lack of variation in training data. The classifier relies on high intersection-over-union (IOU) boxes reported by the RPN to build a model of the category's variation in appearance. With only a few training examples, the variations are insufficient to train the classifier in novel classes. We propose to build a better model of variation in novel classes by transferring the shared within-class variation from base classes. We introduce a hallucinator network and insert it into a modern object detector model, which learns to generate additional training examples in the Region of Interest (ROI's) feature space. Our approach yields significant performance improvements on two state-of-the-art few-shot detectors with different proposal generation processes. We achieve new state-of-the-art in very low-shot regimes on widely used benchmarks PASCAL VOC and COCO.