  Few-shot object detection aims at detecting objects with few annotated
examples, which remains a challenging research problem yet to be explored.
Recent studies have shown the effectiveness of self-learned top-down attention
mechanisms in object detection and other vision tasks. The top-down attention,
however, is less effective at improving the performance of few-shot detectors.
Due to the insufficient training data, object detectors cannot effectively
generate attention maps for few-shot examples. To improve the performance and
interpretability of few-shot object detectors, we propose an attentive few-shot
object detection network (AttFDNet) that takes the advantages of both top-down
and bottom-up attention. Being task-agnostic, the bottom-up attention serves as
a prior that helps detect and localize naturally salient objects. We further
address specific challenges in few-shot object detection by introducing two
novel loss terms and a hybrid few-shot learning strategy. Experimental results
and visualization demonstrate the complementary nature of the two types of
attention and their roles in few-shot object detection. Codes are available at
https://github.com/chenxy99/AttFDNet.
