鲁棒目标检测：基于贝叶斯RetinaNet的同方差随机不确定性建模
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling
摘要：
根据最近的研究，常用的计算机视觉数据集包含约4%的标签错误。例如，COCO数据集以其数据标签中的高噪声水平而闻名，这限制了其在现实世界场景中训练鲁棒神经深度架构的使用。为了模拟这种噪声，本文提出了同方差任意不确定性估计，并提出了一系列新的损失函数，以解决问题的图像目标检测的规模。具体来说，所提出的功能是基于贝叶斯推理，我们已经将它们纳入了共同的社区采用的目标检测深度学习架构RetinaNet。我们还表明，使用我们的新功能的同方差任意不确定性的建模允许增加模型的可解释性，并提高COCO数据集上评估的目标检测性能。
英文摘要：
According to recent studies, commonly used computer vision datasets contain about 4% of label errors. For example, the COCO dataset is known for its high level of noise in data labels, which limits its use for training robust neural deep architectures in a real-world scenario. To model such a noise, in this paper we have proposed the homoscedastic aleatoric uncertainty estimation, and present a series of novel loss functions to address the problem of image object detection at scale. Specifically, the proposed functions are based on Bayesian inference and we have incorporated them into the common community-adopted object detection deep learning architecture RetinaNet. We have also shown that modeling of homoscedastic aleatoric uncertainty using our novel functions allows to increase the model interpretability and to improve the object detection performance being evaluated on the COCO dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
