Sparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object
  Detection
Tahira Shehzadi Khurram Azeem Hashmi Didier Stricker Muhammad Zeshan Afzal
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.