论文标题
meta-drn：1-Shot图像分割的元学习
Meta-DRN: Meta-Learning for 1-Shot Image Segmentation
论文作者
论文摘要
现代深度学习模型彻底改变了计算机视野的领域。但是，大多数这些模型的重要缺点是它们需要大量标记的示例才能正确概括。几次学习的最新发展旨在减轻这一要求。在本文中，我们提出了一种新型的轻质CNN体系结构，用于1张图像分割。提出的模型是通过从良好表现的架构中汲取灵感来创建的，以进行语义细分并将其调整为1-Shot域。我们使用4种元学习算法对模型进行训练，这些算法在图像分类并比较结果方面效果很好。对于所选的数据集，我们提出的模型的参数计数比基准低70％，而使用所有4种元学习算法具有更好或可比的平均分数。
Modern deep learning models have revolutionized the field of computer vision. But, a significant drawback of most of these models is that they require a large number of labelled examples to generalize properly. Recent developments in few-shot learning aim to alleviate this requirement. In this paper, we propose a novel lightweight CNN architecture for 1-shot image segmentation. The proposed model is created by taking inspiration from well-performing architectures for semantic segmentation and adapting it to the 1-shot domain. We train our model using 4 meta-learning algorithms that have worked well for image classification and compare the results. For the chosen dataset, our proposed model has a 70% lower parameter count than the benchmark, while having better or comparable mean IoU scores using all 4 of the meta-learning algorithms.
