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.

我们在遥感图像目标检测中探讨了一种在未筛选的未标记数据上进行半监督学习的方法，该方法使用标记的分布数据动态构建类别特征库，并通过与特征库中的条目进行比较计算 OOD 分数，有效过滤 OOD 样本。通过在 DIOR 和 DOTA 两个广泛使用的遥感目标检测数据集上进行的实验，我们证明了我们提出的方法在 RSIs 中实现开放集半监督目标检测方面具有卓越的性能和效果。

半监督遥感图像目标检测中的非策划未标记数据

Semi-Supervised Object Detection with Uncurated Unlabeled Data for  Remote Sensing Images

Open-set semi-supervised object detection (OSSOD) methods aim to utilize
practical unlabeled datasets with out-of-distribution (OOD) instances for
object detection. The main challenge in OSSOD is distinguishing and filtering
the OOD instances from the in-distribution (ID) instances during
pseudo-labeling. The previous method uses an offline OOD detection network
trained only with labeled data for solving this problem. However, the scarcity
of available data limits the potential for improvement. Meanwhile, training
separately leads to low efficiency. To alleviate the above issues, this paper
proposes a novel end-to-end online framework that improves performance and
efficiency by mining more valuable instances from unlabeled data. Specifically,
we first propose a semi-supervised OOD detection strategy to mine valuable ID
and OOD instances in unlabeled datasets for training. Then, we constitute an
online end-to-end trainable OSSOD framework by integrating the OOD detection
head into the object detector, making it jointly trainable with the original
detection task. Our experimental results show that our method works well on
several benchmarks, including the partially labeled COCO dataset with open-set
classes and the fully labeled COCO dataset with the additional large-scale
open-set unlabeled dataset, OpenImages. Compared with previous OSSOD methods,
our approach achieves the best performance on COCO with OpenImages by +0.94
mAP, reaching 44.07 mAP.

本文提出了一种新的端到端在线架构来改进和提高开放集装箱半监督目标检测的性能和效率，并在 COCO 数据集上取得了最佳表现。