  Annotating remote sensing images (RSIs) presents a notable challenge due to
its labor-intensive nature. Semi-supervised object detection (SSOD) methods
tackle this issue by generating pseudo-labels for the unlabeled data, assuming
that all classes found in the unlabeled dataset are also represented in the
labeled data. However, real-world situations introduce the possibility of
out-of-distribution (OOD) samples being mixed with in-distribution (ID) samples
within the unlabeled dataset. In this paper, we delve into techniques for
conducting SSOD directly on uncurated unlabeled data, which is termed Open-Set
Semi-Supervised Object Detection (OSSOD). Our approach commences by employing
labeled in-distribution data to dynamically construct a class-wise feature bank
(CFB) that captures features specific to each class. Subsequently, we compare
the features of predicted object bounding boxes with the corresponding entries
in the CFB to calculate OOD scores. We design an adaptive threshold based on
the statistical properties of the CFB, allowing us to filter out OOD samples
effectively. The effectiveness of our proposed method is substantiated through
extensive experiments on two widely used remote sensing object detection
datasets: DIOR and DOTA. These experiments showcase the superior performance
and efficacy of our approach for OSSOD on RSIs.
