  This paper presents a Simple and effective unsupervised adaptation method for
Robust Object Detection (SimROD). To overcome the challenging issues of domain
shift and pseudo-label noise, our method integrates a novel domain-centric
augmentation method, a gradual self-labeling adaptation procedure, and a
teacher-guided fine-tuning mechanism. Using our method, target domain samples
can be leveraged to adapt object detection models without changing the model
architecture or generating synthetic data. When applied to image corruptions
and high-level cross-domain adaptation benchmarks, our method outperforms prior
baselines on multiple domain adaptation benchmarks. SimROD achieves new
state-of-the-art on standard real-to-synthetic and cross-camera setup
benchmarks. On the image corruption benchmark, models adapted with our method
achieved a relative robustness improvement of 15-25% AP50 on Pascal-C and 5-6%
AP on COCO-C and Cityscapes-C. On the cross-domain benchmark, our method
outperformed the best baseline performance by up to 8% AP50 on Comic dataset
and up to 4% on Watercolor dataset.
