GOOD: Towards Domain Generalized Orientated Object Detection
This addresses the challenge of deploying oriented object detectors in real-world scenarios with diverse visual styles, though it is incremental as it builds on existing CLIP-based techniques.
The paper tackles the problem of domain generalization in oriented object detection, where detectors trained on one domain fail on unseen domains due to style variations, and proposes GOOD, a method that achieves state-of-the-art performance in multiple cross-domain settings.
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.