Abstract
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 this https URL.
Abstract (translated)
在整个面向对象检测的研究中，一个持续的趋势是追求在使用较少和较弱标注的情况下保持相当的性能。这在遥感领域尤为重要，因为密集的对象分布和众多类别导致了高昂的成本。根据监督水平，现有的面向对象检测算法可以大致分为全监督、半监督和弱监督方法。在此工作中，我们进一步将它们分类为稀疏监督和部分弱监督方法。为了应对大规模标注的挑战，我们引入了第一个稀疏部分弱监督面向对象检测框架，旨在仅利用少量稀疏弱标记数据和大量未标记数据来高效运行。 我们的框架包含三个关键创新： 1. 我们设计了一种稀疏注释方向尺度感知的学生模型（SOS-学生模型），在稀疏标注的情况下将未标注的对象从背景中分离出来，并且能够从无方向性或无尺度性的弱标签中学习到方向和尺度信息。 2. 我们构建了一个新颖的多级伪标签过滤策略，该策略利用了模型预测分布的信息，这些信息来源于模型多层次的预测结果。 3. 我们提出了一种独特的稀疏分区方法，确保每个类别都能得到平等对待。 在DOTA和DIOR数据集上的大量实验表明，我们的框架比上述传统的面向对象检测方法获得了显著的性能提升，并提供了高度成本效益的解决方案。我们公开提供代码：[此链接]（请在此处插入实际的URL）。
URL
https://arxiv.org/abs/2602.03634
