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

Autor:	Shi, Xiangyu, Qiao, Yanyuan, Wu, Qi, Liu, Lingqiao, Dayoub, Feras
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by ECCV workshop ROAM 2024; 12 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.19258 Zobrazit plný text záznamu View this record from Arxiv