  Few-shot object detection (FSOD), which aims at learning a generic detector
that can adapt to unseen tasks with scarce training samples, has witnessed
consistent improvement recently. However, most existing methods ignore the
efficiency issues, e.g., high computational complexity and slow adaptation
speed. Notably, efficiency has become an increasingly important evaluation
metric for few-shot techniques due to an emerging trend toward embedded AI. To
this end, we present an efficient pretrain-transfer framework (PTF) baseline
with no computational increment, which achieves comparable results with
previous state-of-the-art (SOTA) methods. Upon this baseline, we devise an
initializer named knowledge inheritance (KI) to reliably initialize the novel
weights for the box classifier, which effectively facilitates the knowledge
transfer process and boosts the adaptation speed. Within the KI initializer, we
propose an adaptive length re-scaling (ALR) strategy to alleviate the vector
length inconsistency between the predicted novel weights and the pretrained
base weights. Finally, our approach not only achieves the SOTA results across
three public benchmarks, i.e., PASCAL VOC, COCO and LVIS, but also exhibits
high efficiency with 1.8-100x faster adaptation speed against the other methods
on COCO/LVIS benchmark during few-shot transfer. To our best knowledge, this is
the first work to consider the efficiency problem in FSOD. We hope to motivate
a trend toward powerful yet efficient few-shot technique development. The codes
are publicly available at https://github.com/Ze-Yang/Efficient-FSOD.
