A consistent trend throughout the research of oriented object detection has been the pursuit of maintaining comparable performance with fewer and weaker annotations. This is particularly crucial in the remote sensing domain, where the dense object distribution and a wide variety of categories contribute to prohibitively high costs. Based on the supervision level, existing oriented object detection algorithms can be broadly grouped into fully supervised, semi-supervised, and weakly supervised methods. Within the scope of this work, we further categorize them to include sparsely supervised and partially weakly-supervised methods. To address the challenges of large-scale labeling, we introduce the first Sparse Partial Weakly-Supervised Oriented Object Detection framework, designed to efficiently leverage only a few sparse weakly-labeled data and plenty of unlabeled data. Our framework incorporates three key innovations: (1) We design a Sparse-annotation-Orientation-and-Scale-aware Student (SOS-Student) model to separate unlabeled objects from the background in a sparsely-labeled setting, and learn orientation and scale information from orientation-agnostic or scale-agnostic weak annotations. (2) We construct a novel Multi-level Pseudo-label Filtering strategy that leverages the distribution of model predictions, which is informed by the model's multi-layer predictions. (3) We propose a unique sparse partitioning approach, ensuring equal treatment for each category. Extensive experiments on the DOTA and DIOR datasets show that our framework achieves a significant performance gain over traditional oriented object detection methods mentioned above, offering a highly cost-effective solution. Our code is publicly available at https://github.com/VisionXLab/SPWOOD.

翻译：定向目标检测研究中的一个持续趋势是追求以更少、更弱的标注实现可比的性能。这在遥感领域尤为重要，因为密集的目标分布和广泛的类别多样性导致了极高的标注成本。根据监督水平，现有的定向目标检测算法可大致分为全监督、半监督和弱监督方法。在本工作范围内，我们进一步将其细分为稀疏监督和部分弱监督方法。为应对大规模标注的挑战，我们提出了首个稀疏部分弱监督定向目标检测框架，旨在高效利用少量稀疏弱标注数据和大量未标注数据。我们的框架包含三项关键创新：（1）我们设计了一种稀疏标注-方向与尺度感知学生模型（SOS-Student），用于在稀疏标注设置下从未标注背景中分离目标，并从方向无关或尺度无关的弱标注中学习方向与尺度信息。（2）我们构建了一种新颖的多层级伪标签过滤策略，该策略利用模型预测的分布信息，该分布由模型的多层预测提供。（3）我们提出了一种独特的稀疏划分方法，确保每个类别得到平等对待。在DOTA和DIOR数据集上的大量实验表明，我们的框架相较于上述传统定向目标检测方法实现了显著的性能提升，提供了一种极具成本效益的解决方案。我们的代码公开于 https://github.com/VisionXLab/SPWOOD。