Sparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object Detection

Sparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object Detection
Tahira Shehzadi, Khurram Azeem Hashmi, Didier Stricker, Muhammad Zeshan Afzal
In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024. International Conference on Computer Vision and Pattern Recognition (CVPR-2024), June 17-21, Seattle, WA, USA, IEEE/CVF, USA, 6/2024.

Abstract:

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 SemiDETR 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 SemiDETR’s effectiveness in semi-supervised object detection, particularly in challenging scenarios involving small or partially obscured objects.