  Neuroscience studies have shown that the human visual system utilizes
high-level feedback information to guide lower-level perception, enabling
adaptation to signals of different characteristics. In light of this, we
propose Feedback multi-Level feature Extractor (Flex) to incorporate a similar
mechanism for object detection. Flex refines feature selection based on
image-wise and instance-level feedback information in response to image quality
variation and classification uncertainty. Experimental results show that Flex
offers consistent improvement to a range of existing SOTA methods on the
challenging aerial object detection datasets including DOTA-v1.0, DOTA-v1.5,
and HRSC2016. Although the design originates in aerial image detection, further
experiments on MS COCO also reveal our module's efficacy in general detection
models. Quantitative and qualitative analyses indicate that the improvements
are closely related to image qualities, which match our motivation.
