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In this paper, we propose a novel framework for multi-image co-segmentationusing class agnostic meta-learning strategy by generalizing to new classesgiven only a small number of training samples for each new class. We havedeveloped a novel encoder-decoder network termed as DVICE (Directed VariationalInference Cross Encoder), which learns a continuous embedding space to ensurebetter similarity learning. We employ a combination of the proposed DVICEnetwork and a novel few-shot learning approach to tackle the small sample sizeproblem encountered in co-segmentation with small datasets like iCoseg andMSRC. Furthermore, the proposed framework does not use any semantic classlabels and is entirely class agnostic. Through exhaustive experimentation overmultiple datasets using only a small volume of training data, we havedemonstrated that our approach outperforms all existing state-of-the-arttechniques.

Directed Variational Cross-encoder Network for Few-shot Multi-image Co-segmentation