GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection

• URL: http://arxiv.org/abs/2605.29539v2
• Date: Sun, 31 May 2026 07:12:10 GMT
• Title: GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection
• Abstract summary: GiPL is an efficient two-branch training framework for Cross-Domain Few-Shot Object Detection.<n>It consistently outperforms state-of-the-art methods with significant performance gains.
• Score: 11.145284589736635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD). However, they face two critical challenges in fine-tuning: insufficient support set utilization due to sparse single-instance annotations, and severe overfitting under extremely limited target-domain samples. To address these issues, this paper proposes GiPL, an efficient two-branch training framework. In the first branch, we design an iterative pseudo-label self-training paradigm, which performs zero-shot inference on the support set to generate reliable pseudo-annotations, fuses them with ground-truth labels, and iteratively optimizes the model to fully exploit support set data. In the second branch, we introduce generative data augmentation pipeline using large vision-language models, which synthesizes domain-aligned, multi-object annotated images to enrich training samples and suppress overfitting. Extensive experiments on three challenging CD-FSOD datasets (RUOD, CARPK, CarDD) under 1/5/10-shot settings demonstrate that GiPL consistently outperforms state-of-the-art methods with significant performance gains. Code is available at \href{https://github.com/z-yaz/CDiscover}{CDiscover}.

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