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,
and is not trained on the task-specific domain. We validate our approach on
object detection tasks, specifically 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, for instance ranging from geophysics to biology and medicine.

我们提出了一种转移学习框架，通过基于扩散的生成模型在少量真实数据情景下改善目标检测器的性能，从而解决了数据有限性的挑战。该方法在海洋生物学和城市环境中的鱼类和汽车目标检测任务中取得了与数千张图像训练模型相媲美的检测性能，为各个领域的基于生成 AI 的机器学习应用开辟了新的途径。

有限数据集上利用生成模型进行目标检测的迁移学习

Transfer learning with generative models for object detection on limited  datasets

Underwater imagery often exhibits distorted coloration as a result of
light-water interactions, which complicates the study of benthic environments
in marine biology and geography. In this research, we propose an algorithm to
restore the true color (albedo) in underwater imagery by jointly learning the
effects of the medium and neural scene representations. Our approach models
water effects as a combination of light attenuation with distance and
backscattered light. The proposed neural scene representation is based on a
neural reflectance field model, which learns albedos, normals, and volume
densities of the underwater environment. We introduce a logistic regression
model to separate water from the scene and apply distinct light physics during
training. Our method avoids the need to estimate complex backscatter effects in
water by employing several approximations, enhancing sampling efficiency and
numerical stability during training. The proposed technique integrates
underwater light effects into a volume rendering framework with end-to-end
differentiability. Experimental results on both synthetic and real-world data
demonstrate that our method effectively restores true color from underwater
imagery, outperforming existing approaches in terms of color consistency.

通过联合学习介质和神经场表示的效果，我们提出了一种算法来还原水下图像中的真实色彩。我们的方法将水的效应建模为距离和反射光的光衰减的组合。我们的技术能够有效地恢复水下图像的真实色彩，从而提高了采样效率和数值稳定性。