发表机构
Fuzhou University; Xiamen University（福州大学; 厦门大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出PSP-FSOD框架，结合提示驱动域模拟与特征扰动正则化，缓解跨域小样本目标检测的域偏移与标注数据不足问题，在多个基准上实现性能提升。
AI 中文摘要
数据增强通过模拟多样的视觉变化以扩展源分布并引入合成域偏移，是缓解跨域小样本目标检测（CD-FSOD）中严重域偏移与目标标注数据有限问题的简单却有效的策略。现有方法依赖Color-Jitter、Mosaic、Domain-RAG这类常规数据增强，在建模复杂域偏移方面存在局限，且常导致性能欠佳。本文提出PSP-FSOD这一框架，它将提示驱动的域模拟与特征扰动正则化相结合，以提升CD-FSOD的泛化能力。为实现可控的域合成，我们设计了提示驱动策略，利用大型视觉语言模型（VLM）的视觉定位能力联合建模前景与背景变化，生成语义一致但域多样的训练样本；同时采用感知定位的生成方案，引导目标放置并缓解语义-空间错位，进而改善前景适配。为确保训练的稳定性与鲁棒性，我们还引入噪声诱导的特征扰动机制，向多尺度中间特征注入经分布修正的高斯噪声，鼓励模型在扰动下做出一致预测，减少对域特定线索的依赖。大量实验表明，PSP-FSOD能生成高质量的域多样监督信号，学习域不变表示，在多个CD-FSOD基准上均实现了性能提升。
英文摘要
Data augmentation, which simulates diverse visual variations to expand the source distribution and induce synthetic domain shifts, is a simple yet effective strategy for mitigating severe domain shifts and limited labeled target data in cross-domain few-shot object detection (CD-FSOD). Existing approaches rely on conventional data augmentation, such as Color-Jitter, Mosaic, and background-centric adaptation (e.g., Domain-RAG), which are limited in modeling complex domain shifts and often lead to suboptimal performance. In this paper, we propose PSP-FSOD, a principled framework that integrates prompt-driven domain simulation with feature perturbation regularization to improve generalization in CD-FSOD. To enable controllable domain synthesis, we design a prompt-driven strategy that leverages the visual grounding capability of large VLMs to jointly model foreground and background variations, generating semantically consistent yet domain-diverse training samples. Moreover, we adopt a grounding-aware generation scheme that guides object placement and alleviates semantic-spatial misalignment, thereby improving foreground adaptation. To ensure training stability and robustness, we further introduce a noise-induced feature perturbation mechanism that injects Gaussian noise into multi-scale intermediate features with distribution correction, encouraging consistent predictions under perturbations and reducing reliance on domain-specific cues. Extensive experiments demonstrate that PSP-FSOD produces high-quality domain-diverse supervision and learns domain-invariant representations, consistently improving performance across CD-FSOD benchmarks.
