Conventional open-world object detection (OWOD) problem setting first
distinguishes known and unknown classes and then later incrementally learns the
unknown objects when introduced with labels in the subsequent tasks. However,
the current OWOD formulation heavily relies on the external human oracle for
knowledge input during the incremental learning stages. Such reliance on
run-time makes this formulation less realistic in a real-world deployment. To
address this, we introduce a more realistic formulation, named semi-supervised
open-world detection (SS-OWOD), that reduces the annotation cost by casting the
incremental learning stages of OWOD in a semi-supervised manner. We demonstrate
that the performance of the state-of-the-art OWOD detector dramatically
deteriorates in the proposed SS-OWOD setting. Therefore, we introduce a novel
SS-OWOD detector, named SS-OWFormer, that utilizes a feature-alignment scheme
to better align the object query representations between the original and
augmented images to leverage the large unlabeled and few labeled data. We
further introduce a pseudo-labeling scheme for unknown detection that exploits
the inherent capability of decoder object queries to capture object-specific
information. We demonstrate the effectiveness of our SS-OWOD problem setting
and approach for remote sensing object detection, proposing carefully curated
splits and baseline performance evaluations. Our experiments on 4 datasets
including MS COCO, PASCAL, Objects365 and DOTA demonstrate the effectiveness of
our approach. Our source code, models and splits are available here -
this https URL

本研究提出了一种名为 SS-OWOD 的半监督开放世界检测方法，该方法通过减少注释成本，利用特征对齐和伪标签策略在遥感物体检测中取得了良好的效果。

半监督开放世界物体检测

Semi-supervised Open-World Object Detection

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

Remote sensing object detection (RSOD), one of the most fundamental and
challenging tasks in the remote sensing field, has received longstanding
attention. In recent years, deep learning techniques have demonstrated robust
feature representation capabilities and led to a big leap in the development of
RSOD techniques. In this era of rapid technical evolution, this review aims to
present a comprehensive review of the recent achievements in deep learning
based RSOD methods. More than 300 papers are covered in this review. We
identify five main challenges in RSOD, including multi-scale object detection,
rotated object detection, weak object detection, tiny object detection, and
object detection with limited supervision, and systematically review the
corresponding methods developed in a hierarchical division manner. We also
review the widely used benchmark datasets and evaluation metrics within the
field of RSOD, as well as the application scenarios for RSOD. Future research
directions are provided for further promoting the research in RSOD.

遥感物体检测是遥感领域中最基本和具有挑战性的任务之一，深度学习技术在近年来展现出强大的特征表示能力，并推动了遥感物体检测技术的巨大飞跃。本综述旨在全面回顾基于深度学习的遥感物体检测方法的最新成果，涵盖了 300 多篇论文。我们在遥感物体检测中确定了五个主要挑战，包括多尺度物体检测、旋转目标检测、弱目标检测、微小目标检测和受限监督目标检测，并按照层次划分的方式系统回顾了相应的方法。我们还回顾了遥感物体检测领域中广泛使用的基准数据集和评估指标，以及遥感物体检测的应用场景。为进一步推动遥感物体检测研究提供了未来的研究方向。