Uncertainty-Aware Model Adaptation For Unsupervised Cross ... - ArXiv

Computer Science > Computer Vision and Pattern Recognition arXiv:2108.12612 (cs) [Submitted on 28 Aug 2021] Title:Uncertainty-Aware Model Adaptation for Unsupervised Cross-Domain Object Detection Authors:Minjie Cai, Minyi Luo, Xionghu Zhong, Hao Chen View a PDF of the paper titled Uncertainty-Aware Model Adaptation for Unsupervised Cross-Domain Object Detection, by Minjie Cai and 3 other authors View PDF

Abstract:This work tackles the unsupervised cross-domain object detection problem which aims to generalize a pre-trained object detector to a new target domain without labels. We propose an uncertainty-aware model adaptation method, which is based on two motivations: 1) the estimation and exploitation of model uncertainty in a new domain is critical for reliable domain adaptation; and 2) the joint alignment of distributions for inputs (feature alignment) and outputs (self-training) is needed. To this end, we compose a Bayesian CNN-based framework for uncertainty estimation in object detection, and propose an algorithm for generation of uncertainty-aware pseudo-labels. We also devise a scheme for joint feature alignment and self-training of the object detection model with uncertainty-aware pseudo-labels. Experiments on multiple cross-domain object detection benchmarks show that our proposed method achieves state-of-the-art performance.

Comments:	11 pages, 4 figures
Subjects:	Computer Vision and Pattern Recognition (cs.CV)
Cite as:	arXiv:2108.12612 [cs.CV]
(or arXiv:2108.12612v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2108.12612 Focus to learn more arXiv-issued DOI via DataCite

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From: Minjie Cai [view email] [v1] Sat, 28 Aug 2021 09:37:18 UTC (891 KB) Full-text links:

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