Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment
<|reference_start|>Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment: 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<|reference_end|>
@article{danish2024improving,
    title={Improving Single Domain-Generalized Object Detection: A Focus on
  Diversification and Alignment},
    author={Muhammad Sohail Danish, Muhammad Haris Khan, Muhammad Akhtar Munir, M.
  Saquib Sarfraz, Mohsen Ali},
    journal={arXiv preprint arXiv:2405.14497},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14497},
    primaryClass={cs.CV}
}