GOOD: Towards Domain Generalized Orientated Object Detection
Qi Bi Beichen Zhou Jingjun Yi Wei Ji Haolan Zhan Gui-Song Xia
Oriented object detection has been rapidly developed in the past few years,
but most of these methods assume the training and testing images are under the
same statistical distribution, which is far from reality. In this paper, we
propose the task of domain generalized oriented object detection, which intends
to explore the generalization of oriented object detectors on arbitrary unseen
target domains. Learning domain generalized oriented object detectors is
particularly challenging, as the cross-domain style variation not only
negatively impacts the content representation, but also leads to unreliable
orientation predictions. To address these challenges, we propose a generalized
oriented object detector (GOOD). After style hallucination by the emerging
contrastive language-image pre-training (CLIP), it consists of two key
components, namely, rotation-aware content consistency learning (RAC) and style
consistency learning (SEC). The proposed RAC allows the oriented object
detector to learn stable orientation representation from style-diversified
samples. The proposed SEC further stabilizes the generalization ability of
content representation from different image styles. Extensive experiments on
multiple cross-domain settings show the state-of-the-art performance of GOOD.
Source code will be publicly available.