  Incremental few-shot object detection aims at detecting novel classes without
forgetting knowledge of the base classes with only a few labeled training data
from the novel classes. Most related prior works are on incremental object
detection that rely on the availability of abundant training samples per novel
class that substantially limits the scalability to real-world setting where
novel data can be scarce. In this paper, we propose the Incremental-DETR that
does incremental few-shot object detection via fine-tuning and self-supervised
learning on the DETR object detector. To alleviate severe over-fitting with few
novel class data, we first fine-tune the class-specific components of DETR with
self-supervision from additional object proposals generated using Selective
Search as pseudo labels. We further introduce an incremental few-shot
fine-tuning strategy with knowledge distillation on the class-specific
components of DETR to encourage the network in detecting novel classes without
forgetting the base classes. Extensive experiments conducted on standard
incremental object detection and incremental few-shot object detection settings
show that our approach significantly outperforms state-of-the-art methods by a
large margin.
