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. Our code and models are available at: https://github.com/msohaildanish/DivAlign
Cite
@article{arxiv.2405.14497,
title = {Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment},
author = {Muhammad Sohail Danish and Muhammad Haris Khan and Muhammad Akhtar Munir and M. Saquib Sarfraz and Mohsen Ali},
journal= {arXiv preprint arXiv:2405.14497},
year = {2024}
}