  In this work, we tackle the problem of domain generalization for object
detection, specifically focusing on the scenario where only a single source
domain is available. We propose an effective approach that involves two key
steps: diversifying the source domain and aligning detections based on class
prediction confidence and localization. Firstly, we demonstrate that by
carefully selecting a set of augmentations, a base detector can outperform
existing methods for single domain generalization by a good margin. This
highlights the importance of domain diversification in improving the
performance of object detectors. Secondly, we introduce a method to align
detections from multiple views, considering both classification and
localization outputs. This alignment procedure leads to better generalized and
well-calibrated object detector models, which are crucial for accurate
decision-making in safety-critical applications. Our approach is
detector-agnostic and can be seamlessly applied to both single-stage and
two-stage detectors. To validate the effectiveness of our proposed methods, we
conduct extensive experiments and ablations on challenging domain-shift
scenarios. The results consistently demonstrate the superiority of our approach
compared to existing methods. Our code and models are available at:
https://github.com/msohaildanish/DivAlign
