  We study adapting trained object detectors to unseen domains manifesting
significant variations of object appearance, viewpoints and backgrounds. Most
current methods align domains by either using image or instance-level feature
alignment in an adversarial fashion. This often suffers due to the presence of
unwanted background and as such lacks class-specific alignment. A common remedy
to promote class-level alignment is to use high confidence predictions on the
unlabelled domain as pseudo labels. These high confidence predictions are often
fallacious since the model is poorly calibrated under domain shift. In this
paper, we propose to leverage model predictive uncertainty to strike the right
balance between adversarial feature alignment and class-level alignment.
Specifically, we measure predictive uncertainty on class assignments and the
bounding box predictions. Model predictions with low uncertainty are used to
generate pseudo-labels for self-supervision, whereas the ones with higher
uncertainty are used to generate tiles for an adversarial feature alignment
stage. This synergy between tiling around the uncertain object regions and
generating pseudo-labels from highly certain object regions allows us to
capture both the image and instance level context during the model adaptation
stage. We perform extensive experiments covering various domain shift
scenarios. Our approach improves upon existing state-of-the-art methods with
visible margins.
