Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929355047829504 |
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| author | Danish, Muhammad Sohail Khan, Muhammad Haris Munir, Muhammad Akhtar Sarfraz, M. Saquib Ali, Mohsen |
| author_facet | Danish, Muhammad Sohail Khan, Muhammad Haris Munir, Muhammad Akhtar Sarfraz, M. Saquib Ali, Mohsen |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14497 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment Danish, Muhammad Sohail Khan, Muhammad Haris Munir, Muhammad Akhtar Sarfraz, M. Saquib Ali, Mohsen Computer Vision and Pattern Recognition 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 |
| title | Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.14497 |