  Conventional deep learning based methods for object detection require a large
amount of bounding box annotations for training, which is expensive to obtain
such high quality annotated data. Few-shot object detection, which learns to
adapt to novel classes with only a few annotated examples, is very challenging
since the fine-grained feature of novel object can be easily overlooked with
only a few data available. In this work, aiming to fully exploit features of
annotated novel object and capture fine-grained features of query object, we
propose Dense Relation Distillation with Context-aware Aggregation (DCNet) to
tackle the few-shot detection problem. Built on the meta-learning based
framework, Dense Relation Distillation module targets at fully exploiting
support features, where support features and query feature are densely matched,
covering all spatial locations in a feed-forward fashion. The abundant usage of
the guidance information endows model the capability to handle common
challenges such as appearance changes and occlusions. Moreover, to better
capture scale-aware features, Context-aware Aggregation module adaptively
harnesses features from different scales for a more comprehensive feature
representation. Extensive experiments illustrate that our proposed approach
achieves state-of-the-art results on PASCAL VOC and MS COCO datasets. Code will
be made available at https://github.com/hzhupku/DCNet.
