  Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a
model trained on a source domain to detect instances from a new target domain
for which annotations are not available. Different from traditional approaches,
we propose ConfMix, the first method that introduces a sample mixing strategy
based on region-level detection confidence for adaptive object detector
learning. We mix the local region of the target sample that corresponds to the
most confident pseudo detections with a source image, and apply an additional
consistency loss term to gradually adapt towards the target data distribution.
In order to robustly define a confidence score for a region, we exploit the
confidence score per pseudo detection that accounts for both the
detector-dependent confidence and the bounding box uncertainty. Moreover, we
propose a novel pseudo labelling scheme that progressively filters the pseudo
target detections using the confidence metric that varies from a loose to
strict manner along the training. We perform extensive experiments with three
datasets, achieving state-of-the-art performance in two of them and approaching
the supervised target model performance in the other. Code is available at:
https://github.com/giuliomattolin/ConfMix.
