Sparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object Detection
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arXiv
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| Format: | Preprint |
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2024
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| author | Shehzadi, Tahira Hashmi, Khurram Azeem Stricker, Didier Afzal, Muhammad Zeshan |
| author_facet | Shehzadi, Tahira Hashmi, Khurram Azeem Stricker, Didier Afzal, Muhammad Zeshan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01819 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object Detection Shehzadi, Tahira Hashmi, Khurram Azeem Stricker, Didier Afzal, Muhammad Zeshan Computer Vision and Pattern Recognition 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. |
| title | Sparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.01819 |