Uncertainty-Aware Model Adaptation for Unsupervised Cross-Domain Object Detection

• URL: http://arxiv.org/abs/2108.12612v1
• Date: Sat, 28 Aug 2021 09:37:18 GMT
• Title: Uncertainty-Aware Model Adaptation for Unsupervised Cross-Domain Object Detection
• Authors: Minjie Cai, Minyi Luo, Xionghu Zhong, Hao Chen
• Abstract summary: This work tackles the unsupervised cross-domain object detection problem. It aims to generalize a pre-trained object detector to a new target domain without labels.
• Score: 12.807987076435928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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