  Object detection is one of the most fundamental yet challenging research
topics in the domain of computer vision. Recently, the study on this topic in
aerial images has made tremendous progress. However, complex background and
worse imaging quality are obvious problems in aerial object detection. Most
state-of-the-art approaches tend to develop elaborate attention mechanisms for
the space-time feature calibrations with arduous computational complexity,
while surprisingly ignoring the importance of feature calibrations in
channel-wise. In this work, we propose a simple yet effective
Calibrated-Guidance (CG) scheme to enhance channel communications in a feature
transformer fashion, which can adaptively determine the calibration weights for
each channel based on the global feature affinity correlations. Specifically,
for a given set of feature maps, CG first computes the feature similarity
between each channel and the remaining channels as the intermediary calibration
guidance. Then, re-representing each channel by aggregating all the channels
weighted together via the guidance operation. Our CG is a general module that
can be plugged into any deep neural networks, which is named as CG-Net. To
demonstrate its effectiveness and efficiency, extensive experiments are carried
out on both oriented object detection task and horizontal object detection task
in aerial images. Experimental results on two challenging benchmarks (DOTA and
HRSC2016) demonstrate that our CG-Net can achieve the new state-of-the-art
performance in accuracy with a fair computational overhead. The source code has
been open sourced at https://github.com/WeiZongqi/CG-Net
