  Few-shot object detection (FSOD) is a challenging problem aimed at detecting
novel concepts from few exemplars. Existing approaches to FSOD all assume
abundant base labels to adapt to novel objects. This paper studies the new task
of semi-supervised FSOD by considering a realistic scenario in which both base
and novel labels are simultaneously scarce. We explore the utility of unlabeled
data within our proposed label-efficient detection framework and discover its
remarkable ability to boost semi-supervised FSOD by way of region proposals.
Motivated by this finding, we introduce SoftER Teacher, a robust detector
combining pseudo-labeling with consistency learning on region proposals, to
harness unlabeled data for improved FSOD without relying on abundant labels.
Rigorous experiments show that SoftER Teacher surpasses the novel performance
of a strong supervised detector using only 10% of required base labels, without
catastrophic forgetting observed in prior approaches. Our work also sheds light
on a potential relationship between semi-supervised and few-shot detection
suggesting that a stronger semi-supervised detector leads to a more effective
few-shot detector.
