论文标题
SML：用于几个弹性语义分段的语义元学习
SML: Semantic Meta-learning for Few-shot Semantic Segmentation
论文作者
论文摘要
培训卷积神经网络所需的大量培训数据已成为语义分割等应用的瓶颈。很少有语义分割算法解决了这个问题，目的是在低数据表中实现良好的性能，而带注释的培训图像很少。最近，基于根据可用培训数据计算出的类 - 概况的方法为这项任务取得了巨大的成功。在这项工作中，我们提出了一个新型的元学习框架，语义元学习（SML），该框架将类别的语义描述包含在生成的原型中，以解决此问题。此外，我们建议使用良好的技术Ridge回归，不仅引入了类级的语义信息，而且还可以有效利用从培训数据中的多个图像中获得的原型计算中可用的信息。这具有简单的封闭式解决方案，因此可以轻松有效地实现。在不同实验设置下的基准Pascal-5i数据集上进行的广泛实验显示了所提出的框架的有效性。
The significant amount of training data required for training Convolutional Neural Networks has become a bottleneck for applications like semantic segmentation. Few-shot semantic segmentation algorithms address this problem, with an aim to achieve good performance in the low-data regime, with few annotated training images. Recently, approaches based on class-prototypes computed from available training data have achieved immense success for this task. In this work, we propose a novel meta-learning framework, Semantic Meta-Learning (SML) which incorporates class level semantic descriptions in the generated prototypes for this problem. In addition, we propose to use the well established technique, ridge regression, to not only bring in the class-level semantic information, but also to effectively utilise the information available from multiple images present in the training data for prototype computation. This has a simple closed-form solution, and thus can be implemented easily and efficiently. Extensive experiments on the benchmark PASCAL-5i dataset under different experimental settings show the effectiveness of the proposed framework.
