MessDet shows that strict rotation equivariance in backbone and neck improves aerial detection accuracy over approximate equivariance, achieving SOTA on DOTA-v1.0/v1.5 and DIOR-R with 18.1M parameters.
Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images
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Measuring the Impact of Rotation Equivariance on Aerial Object Detection
MessDet shows that strict rotation equivariance in backbone and neck improves aerial detection accuracy over approximate equivariance, achieving SOTA on DOTA-v1.0/v1.5 and DIOR-R with 18.1M parameters.