  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.
