论文标题
定向变异的跨编码网络，用于几个射击的多图像共段
Directed Variational Cross-encoder Network for Few-shot Multi-image Co-segmentation
论文作者
论文摘要
在本文中，我们提出了一个新的框架，用于使用类不可知论的元学习策略，通过将新类推广到新的类别，只给出了每个新班级的少量培训样本，从而将多图像进行分割。我们已经开发了一种新颖的编码器 - 模块网络，称为DVICE（定向变异推理交叉编码器），该网络学习了一个连续的嵌入空间，以确保更好的相似性学习。我们采用了提出的DVICE网络和一种新颖的几声学习方法的组合来解决与Icoseg和MSRC等小数据集中遇到的小样本量问题。此外，所提出的框架不使用任何语义类标签，并且完全是阶级的不可知论。通过仅使用少量培训数据对多个数据集进行详尽的实验，我们证明了我们的方法的表现优于所有现有的最新技术。
In this paper, we propose a novel framework for multi-image co-segmentation using class agnostic meta-learning strategy by generalizing to new classes given only a small number of training samples for each new class. We have developed a novel encoder-decoder network termed as DVICE (Directed Variational Inference Cross Encoder), which learns a continuous embedding space to ensure better similarity learning. We employ a combination of the proposed DVICE network and a novel few-shot learning approach to tackle the small sample size problem encountered in co-segmentation with small datasets like iCoseg and MSRC. Furthermore, the proposed framework does not use any semantic class labels and is entirely class agnostic. Through exhaustive experimentation over multiple datasets using only a small volume of training data, we have demonstrated that our approach outperforms all existing state-of-the-art techniques.
