  Conventional detection networks usually need abundant labeled training
samples, while humans can learn new concepts incrementally with just a few
examples. This paper focuses on a more challenging but realistic
class-incremental few-shot object detection problem (iFSD). It aims to
incrementally transfer the model for novel objects from only a few annotated
samples without catastrophically forgetting the previously learned ones. To
tackle this problem, we propose a novel method LEAST, which can transfer with
Less forgetting, fEwer training resources, And Stronger Transfer capability.
Specifically, we first present the transfer strategy to reduce unnecessary
weight adaptation and improve the transfer capability for iFSD. On this basis,
we then integrate the knowledge distillation technique using a less
resource-consuming approach to alleviate forgetting and propose a novel
clustering-based exemplar selection process to preserve more discriminative
features previously learned. Being a generic and effective method, LEAST can
largely improve the iFSD performance on various benchmarks.
