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arXiv · 2505.00938

CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion

Boyuan Meng·Xiaohan Zhang·Peilin Li·Zhe Wu·Yiming Li·Wenkai Zhao·Beinan Yu·Hui-Liang Shen
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Abstract

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.

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Boyuan Meng, Xiaohan Zhang, Peilin Li, Zhe Wu, Yiming Li, Wenkai Zhao, Beinan Yu, Hui-Liang Shen. 2025-05-02. CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion. https://arxiv.org/abs/2505.00938

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