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.

Sparse Semi-DETR 是一种基于 Transformer 的端到端半监督目标检测解决方案，通过引入查询精化模块和可靠伪标签过滤模块来解决 DETR-based SSOD 框架中对象查询质量不准确和重叠预测的问题，大大提高了对小型和部分遮挡对象的检测能力，并在 MS-COCO 和 Pascal VOC 目标检测基准测试中实现了显著改进。