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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.00938 |
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| _version_ | 1866912357593120768 |
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| author | Meng, Boyuan Zhang, Xiaohan Li, Peilin Wu, Zhe Li, Yiming Zhao, Wenkai Yu, Beinan Shen, Hui-Liang |
| author_facet | Meng, Boyuan Zhang, Xiaohan Li, Peilin Wu, Zhe Li, Yiming Zhao, Wenkai Yu, Beinan Shen, Hui-Liang |
| contents | Cross-domain few-shot object detection (CD-FSOD) aims to detect novel objects across different domains with limited class instances. Feature confusion, including object-background confusion and object-object confusion, presents significant challenges in both cross-domain and few-shot settings. In this work, we introduce CDFormer, a cross-domain few-shot object detection transformer against feature confusion, to address these challenges. The method specifically tackles feature confusion through two key modules: object-background distinguishing (OBD) and object-object distinguishing (OOD). The OBD module leverages a learnable background token to differentiate between objects and background, while the OOD module enhances the distinction between objects of different classes. Experimental results demonstrate that CDFormer outperforms previous state-of-the-art approaches, achieving 12.9% mAP, 11.0% mAP, and 10.4% mAP improvements under the 1/5/10 shot settings, respectively, when fine-tuned. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00938 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion Meng, Boyuan Zhang, Xiaohan Li, Peilin Wu, Zhe Li, Yiming Zhao, Wenkai Yu, Beinan Shen, Hui-Liang Computer Vision and Pattern Recognition Artificial Intelligence Cross-domain few-shot object detection (CD-FSOD) aims to detect novel objects across different domains with limited class instances. Feature confusion, including object-background confusion and object-object confusion, presents significant challenges in both cross-domain and few-shot settings. In this work, we introduce CDFormer, a cross-domain few-shot object detection transformer against feature confusion, to address these challenges. The method specifically tackles feature confusion through two key modules: object-background distinguishing (OBD) and object-object distinguishing (OOD). The OBD module leverages a learnable background token to differentiate between objects and background, while the OOD module enhances the distinction between objects of different classes. Experimental results demonstrate that CDFormer outperforms previous state-of-the-art approaches, achieving 12.9% mAP, 11.0% mAP, and 10.4% mAP improvements under the 1/5/10 shot settings, respectively, when fine-tuned. |
| title | CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2505.00938 |