Boosting Semi-Supervised Few-Shot Object Detection with SoftER Teacher

Few-shot object detection is an emerging problem aimed at detecting novel concepts from few exemplars. Existing approaches to few-shot detection assume abundant base labels to adapt to novel objects. This paper explores the task of semi-supervised few-shot detection by considering a realistic scenario which lacks abundant labels for both base and novel objects. Motivated by this unique problem, we introduce SoftER Teacher, a robust detector combining the advantages of pseudo-labeling with representation learning on region proposals. SoftER Teacher harnesses unlabeled data to jointly optimize for semi-supervised few-shot detection without explicitly relying on abundant base labels. Extensive experiments show that SoftER Teacher matches the novel class performance of a strong supervised detector using only 10% of base labels. Our work also sheds insight into a previously unknown relationship between semi-supervised and few-shot detection to suggest that a stronger semi-supervised detector leads to a more label-efficient few-shot detector. Code and models are available at https://github.com/lexisnexis-risk-open-source/ledetection