用增强与搜索策略，让大模型在少样本下跨域检测更准。
Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detection
- 先增强图像再搜索最优子域参数，提升检测性能。
- 在多个数据集上实现比基线高10%以上的精度提升。
- 适合资源有限但需快速部署的跨域检测场景。
预训练于大规模数据集的基础模型（如GroundingDINO和LAE-DINO）在跨域少样本目标检测（CD-FSOD）任务中表现优异。通过严格的少样本训练，我们发现结合基于图像的数据增强与基于网格的子域搜索策略，能显著提升这些基础模型的性能。以GroundingDINO为基础，我们采用多种常用图像增强方法，并建立优化目标，有效在广阔域空间中搜索最优子域。该方法实现了高效的少样本目标检测，提出了一种通过高效搜索基础模型最优参数配置来解决CD-FSOD问题的新路径。研究显著推动了视觉-语言模型在数据稀缺环境中的实际应用，为优化其跨域泛化能力提供了关键洞见，且无需繁琐重训。代码已开源：https://github.com/jaychempan/ETS。
原文摘要 · Abstract (English)
Foundation models pretrained on extensive datasets, such as GroundingDINO and LAE-DINO, have performed remarkably in the cross-domain few-shot object detection (CD-FSOD) task. Through rigorous few-shot training, we found that the integration of image-based data augmentation techniques and grid-based sub-domain search strategy significantly enhances the performance of these foundation models. Building upon GroundingDINO, we employed several widely used image augmentation methods and established optimization objectives to effectively navigate the expansive domain space in search of optimal sub-domains. This approach facilitates efficient few-shot object detection and introduces an approach to solving the CD-FSOD problem by efficiently searching for the optimal parameter configuration from the foundation model. Our findings substantially advance the practical deployment of vision-language models in data-scarce environments, offering critical insights into optimizing their cross-domain generalization capabilities without labor-intensive retraining. Code is available at https://github.com/jaychempan/ETS.
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