In this paper, we address the limitations of the DETR-based semi-supervised object detection (SSOD) framework, particularly focusing on the challenges posed by the quality of object queries. In DETR-based SSOD, the one-to-one assignment strategy provides inaccurate pseudo-labels, while the one-to-many assignments strategy leads to overlapping predictions. These issues compromise training efficiency and degrade model performance, especially in detecting small or occluded objects. We introduce Sparse Semi-DETR, a novel transformer-based, end-to-end semi-supervised object detection solution to overcome these challenges. Sparse Semi-DETR incorporates a Query Refinement Module to enhance the quality of object queries, significantly improving detection capabilities for small and partially obscured objects. Additionally, we integrate a Reliable Pseudo-Label Filtering Module that selectively filters high-quality pseudo-labels, thereby enhancing detection accuracy and consistency. On the MS-COCO and Pascal VOC object detection benchmarks, Sparse Semi-DETR achieves a significant improvement over current state-of-the-art methods that highlight Sparse Semi-DETR's effectiveness in semi-supervised object detection, particularly in challenging scenarios involving small or partially obscured objects.

翻译：本文针对基于DETR的半监督目标检测（SSOD）框架的局限性进行了研究，特别关注目标查询质量带来的挑战。在基于DETR的SSOD中，一对一分配策略产生不准确的伪标签，而一对多分配策略则导致预测重叠。这些问题损害了训练效率，降低了模型性能，尤其是在检测小目标或遮挡目标时。我们提出了稀疏半DETR（Sparse Semi-DETR），这是一种新颖的基于Transformer的端到端半监督目标检测解决方案，以克服这些挑战。稀疏半DETR引入了查询精炼模块（Query Refinement Module）来提升目标查询的质量，显著增强了对小目标和部分遮挡目标的检测能力。此外，我们还集成了一个可靠伪标签过滤模块（Reliable Pseudo-Label Filtering Module），该模块能够有选择性地过滤高质量伪标签，从而提升检测精度和一致性。在MS-COCO和Pascal VOC目标检测基准测试中，稀疏半DETR相比当前最先进方法取得了显著改进，突显了其在半监督目标检测中的有效性，尤其在涉及小目标或部分遮挡目标的挑战性场景中表现优异。