English(EN) GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection

新的GiPL方法通过生成增强提升小样本目标检测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-29 04:00

研究人员推出了一种名为GiPL的新型跨域小样本目标检测（CD-FSOD）方法，该方法解决了利用有限支持集数据和防止过拟合的挑战。GiPL采用双分支训练策略：一个分支使用迭代伪标签自训练，从稀疏数据生成和精炼标注；另一个分支则利用大型视觉语言模型合成多样化、领域对齐的图像用于数据增强。在多个数据集上的实验表明，GiPL在各种小样本设置下显著优于现有的最先进方法。 AI

排序理由这是一篇研究论文，详细介绍了一种新的目标检测方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Jiacong Liu, Shu Luo, Yikai Qin, Yaze Zhao, Yongwei Jiang, Yixiong Zou · 2026-05-29 04:00

GiPL：用于跨域少样本目标检测的生成增强迭代伪标签

arXiv:2605.29539v1 Announce Type: cross 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 …

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GiPL：用于跨域少样本目标检测的生成增强迭代伪标签

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