  Source-Free domain adaptive Object Detection (SFOD) is a promising strategy
for deploying trained detectors to new, unlabeled domains without accessing
source data, addressing significant concerns around data privacy and
efficiency. Most SFOD methods leverage a Mean-Teacher (MT) self-training
paradigm relying heavily on High-confidence Pseudo Labels (HPL). However, these
HPL often overlook small instances that undergo significant appearance changes
with domain shifts. Additionally, HPL ignore instances with low confidence due
to the scarcity of training samples, resulting in biased adaptation toward
familiar instances from the source domain. To address this limitation, we
introduce the Low-confidence Pseudo Label Distillation (LPLD) loss within the
Mean-Teacher based SFOD framework. This novel approach is designed to leverage
the proposals from Region Proposal Network (RPN), which potentially encompasses
hard-to-detect objects in unfamiliar domains. Initially, we extract HPL using a
standard pseudo-labeling technique and mine a set of Low-confidence Pseudo
Labels (LPL) from proposals generated by RPN, leaving those that do not overlap
significantly with HPL. These LPL are further refined by leveraging
class-relation information and reducing the effect of inherent noise for the
LPLD loss calculation. Furthermore, we use feature distance to adaptively
weight the LPLD loss to focus on LPL containing a larger foreground area. Our
method outperforms previous SFOD methods on four cross-domain object detection
benchmarks. Extensive experiments demonstrate that our LPLD loss leads to
effective adaptation by reducing false negatives and facilitating the use of
domain-invariant knowledge from the source model. Code is available at
https://github.com/junia3/LPLD.
