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[bibtex]@InProceedings{Danish_2024_CVPR, author = {Danish, Muhammad Sohail and Khan, Muhammad Haris and Munir, Muhammad Akhtar and Sarfraz, M. Saquib and Ali, Mohsen}, title = {Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {17732-17742} }
Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment
Abstract
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.
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