  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.
