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arXiv · 2310.19258

Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisition

Xiangyu Shi·Yanyuan Qiao·Qi Wu·Lingqiao Liu·Feras Dayoub
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Abstract

Effective object detection in autonomous vehicles is challenged by deployment in diverse and unfamiliar environments. Online Source-Free Domain Adaptation (O-SFDA) offers model adaptation using a stream of unlabeled data from a target domain in an online manner. However, not all captured frames contain information beneficial for adaptation, especially in the presence of redundant data and class imbalance issues. This paper introduces a novel approach to enhance O-SFDA for adaptive object detection through unsupervised data acquisition. Our methodology prioritizes the most informative unlabeled frames for inclusion in the online training process. Empirical evaluation on a real-world dataset reveals that our method outperforms existing state-of-the-art O-SFDA techniques, demonstrating the viability of unsupervised data acquisition for improving the adaptive object detector.

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Xiangyu Shi, Yanyuan Qiao, Qi Wu, Lingqiao Liu, Feras Dayoub. 2024-08-30. Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisition. https://arxiv.org/abs/2310.19258

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