| Summary: | This paper proposes a new method for addressing the problem of unsupervised domain adaptation for robust object detection. To this end, we propose an energy-based curriculum for progressively adapting a model, thereby mitigating the pseudo-label noise caused by domain shifts. Throughout the adaptation process, we also make use of spatial domain mixing as well as knowledge distillation to improve the pseudo-labels reliability. Our method does not require any modifications in the model architecture or any special training tricks or complications. Our end-to-end pipeline, although simple, proves effective in adapting object detector neural networks. To verify our method, we perform an extensive systematic set of experiments on: synthetic-to-real scenario, cross-camera setup, cross-domain artistic datasets, and image corruption benchmarks, and establish a new state-of-the-art in several cases. For example, compared to the best existing baselines, our Energy-Based Curriculum learning method for robust object Detection (EBCDet), achieves: 1–3 % AP50 improvement on Sim10k-to-Cityscapes and KITTI-to-Cityscapes, 3–4 % AP50 boost on Pascal-VOC-to- Comic, WaterColor, and ClipArt, and 1-5% relative robustness improvement on Pascal-C, COCO-C, and Cityscapes-C (1-2 % absolute mPC). Code is available at: <uri>https://github.com/AutomotiveML/EBCDet</uri>.
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