  Learning from a few training samples is a desirable ability of an object
detector, inspiring the explorations of Few-Shot Object Detection (FSOD). Most
existing approaches employ a pretrain-transfer paradigm. The model is first
pre-trained on base classes with abundant data and then transferred to novel
classes with a few annotated samples. Despite the substantial progress, the
FSOD performance is still far behind satisfactory. During pre-training, due to
the co-occurrence between base and novel classes, the model is learned to treat
the co-occurred novel classes as backgrounds. During transferring, given scarce
samples of novel classes, the model suffers from learning discriminative
features to distinguish novel instances from backgrounds and base classes. To
overcome the obstacles, we propose a novel framework, Mining Implicit Novel
Instances (MINI), to mine the implicit novel instances as auxiliary training
samples, which widely exist in abundant base data but are not annotated. MINI
comprises an offline mining mechanism and an online mining mechanism. The
offline mining mechanism leverages a self-supervised discriminative model to
collaboratively mine implicit novel instances with a trained FSOD network.
Taking the mined novel instances as auxiliary training samples, the online
mining mechanism takes a teacher-student framework to simultaneously update the
FSOD network and the mined implicit novel instances on the fly. Extensive
experiments on PASCAL VOC and MS-COCO datasets show MINI achieves new
state-of-the-art performance on any shot and split. The significant performance
improvements demonstrate the superiority of our method.
