  Recently, the study on object detection in aerial images has made tremendous
progress in the community of computer vision. However, most state-of-the-art
methods tend to develop elaborate attention mechanisms for the space-time
feature calibrations with high computational complexity, while surprisingly
ignoring the importance of feature calibrations in channels. 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-pairs. Specifically, given a 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. Our CG can be
plugged into any deep neural network, which is named as CG-Net. To demonstrate
its effectiveness and efficiency, extensive experiments are carried out on both
oriented and horizontal object detection tasks of aerial images. Results on two
challenging benchmarks (i.e., DOTA and HRSC2016) demonstrate that our CG-Net
can achieve state-of-the-art performance in accuracy with a fair computational
overhead. https://github.com/WeiZongqi/CG-Net
