The availability of data is limited in some fields, especially for object detection tasks, where it is necessary to have correctly labeled bounding boxes around each object. A notable example of such data scarcity is found in the domain of marine biology, where it is useful to develop methods to automatically detect submarine species for environmental monitoring. To address this data limitation, the state-of-the-art machine learning strategies employ two main approaches. The first involves pretraining models on existing datasets before generalizing to the specific domain of interest. The second strategy is to create synthetic datasets specifically tailored to the target domain using methods like copy-paste techniques or ad-hoc simulators. The first strategy often faces a significant domain shift, while the second demands custom solutions crafted for the specific task. In response to these challenges, here we propose a transfer learning framework that is valid for a generic scenario. In this framework, generated images help to improve the performances of an object detector in a few-real data regime. This is achieved through a diffusion-based generative model that was pretrained on large generic datasets. With respect to the state-of-the-art, we find that it is not necessary to fine tune the generative model on the specific domain of interest. We believe that this is an important advance because it mitigates the labor-intensive task of manual labeling the images in object detection tasks. We validate our approach focusing on fishes in an underwater environment, and on the more common domain of cars in an urban setting. Our method achieves detection performance comparable to models trained on thousands of images, using only a few hundreds of input data. Our results pave the way for new generative AI-based protocols for machine learning applications in various domains.

翻译：在某些领域中，数据的可获得性受到限制，尤其是在目标检测任务中，需要对每个物体进行精确标注的边界框。这种数据稀缺性的一个显著例子出现在海洋生物学领域，开发自动检测水下物种的方法对环境监测具有重要意义。为应对这一数据限制，当前最先进的机器学习策略主要采用两种方法。第一种方法是在现有数据集上对模型进行预训练，然后泛化到特定目标领域。第二种策略是通过复制粘贴技术或专用模拟器等方法，创建专门针对目标领域的合成数据集。第一种方法常面临显著的领域偏移问题，而第二种方法则需要针对特定任务定制解决方案。为应对这些挑战，本文提出一种适用于通用场景的迁移学习框架。在该框架中，生成的图像有助于提升目标检测器在少量真实数据条件下的性能。这是通过一个基于扩散的生成模型实现的，该模型已在大型通用数据集上完成预训练。相较于现有技术，我们发现无需在特定目标领域对生成模型进行微调。我们认为这是一项重要进展，因为它减轻了目标检测任务中手动标注图像所需的高强度劳动。我们在水下环境中的鱼类检测以及更常见的城市环境车辆检测两个场景中验证了该方法。仅使用数百个输入数据，我们的方法就能达到与数千张图像训练模型相当的检测性能。我们的研究结果为各领域机器学习应用开辟了基于生成式人工智能的新协议路径。