  Domain adaptive object detection (DAOD) assumes that both labeled source data
and unlabeled target data are available for training, but this assumption does
not always hold in real-world scenarios. Thus, source-free DAOD is proposed to
adapt the source-trained detectors to target domains with only unlabeled target
data. Existing source-free DAOD methods typically utilize pseudo labeling,
where the performance heavily relies on the selection of confidence threshold.
However, most prior works adopt a single fixed threshold for all classes to
generate pseudo labels, which ignore the imbalanced class distribution,
resulting in biased pseudo labels. In this work, we propose a refined pseudo
labeling framework for source-free DAOD. First, to generate unbiased pseudo
labels, we present a category-aware adaptive threshold estimation module, which
adaptively provides the appropriate threshold for each category. Second, to
alleviate incorrect box regression, a localization-aware pseudo label
assignment strategy is introduced to divide labels into certain and uncertain
ones and optimize them separately. Finally, extensive experiments on four
adaptation tasks demonstrate the effectiveness of our method.
