Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection

• URL: http://arxiv.org/abs/2103.00236v1
• Date: Sat, 27 Feb 2021 15:04:07 GMT
• Title: Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection
• Authors: Dayan Guan, Jiaxing Huang, Aoran Xiao, Shijian Lu, Yanpeng Cao
• Abstract summary: Unlabelled domain adaptive object detection aims to adapt detectors from a labelled source domain to an unsupervised target domain. adversarial learning may impair the alignment of well-aligned samples as it merely aligns the global distributions across domains. We design an uncertainty-aware domain adaptation network (UaDAN) that introduces conditional adversarial learning to align well-aligned and poorly-aligned samples separately.
• Score: 34.18382705952121
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unsupervised domain adaptive object detection aims to adapt detectors from a labelled source domain to an unlabelled target domain. Most existing works take a two-stage strategy that first generates region proposals and then detects objects of interest, where adversarial learning is widely adopted to mitigate the inter-domain discrepancy in both stages. However, adversarial learning may impair the alignment of well-aligned samples as it merely aligns the global distributions across domains. To address this issue, we design an uncertainty-aware domain adaptation network (UaDAN) that introduces conditional adversarial learning to align well-aligned and poorly-aligned samples separately in different manners. Specifically, we design an uncertainty metric that assesses the alignment of each sample and adjusts the strength of adversarial learning for well-aligned and poorly-aligned samples adaptively. In addition, we exploit the uncertainty metric to achieve curriculum learning that first performs easier image-level alignment and then more difficult instance-level alignment progressively. Extensive experiments over four challenging domain adaptive object detection datasets show that UaDAN achieves superior performance as compared with state-of-the-art methods.

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