Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment

• URL: http://arxiv.org/abs/2405.14497v1
• Date: Thu, 23 May 2024 12:29:25 GMT
• Title: Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment
• Authors: Muhammad Sohail Danish, Muhammad Haris Khan, Muhammad Akhtar Munir, M. Saquib Sarfraz, Mohsen Ali,
• Abstract summary: A base detector can outperform existing methods for single domain generalization by a good margin. We introduce a method to align detections from multiple views, considering both classification and localization outputs. Our approach is detector-agnostic and can be seamlessly applied to both single-stage and two-stage detectors.
• Score: 17.485775402656127
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Our code and models are available at: https://github.com/msohaildanish/DivAlign

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