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.
@article{arxiv.2010.08800,
title = {Directed Variational Cross-encoder Network for Few-shot Multi-image Co-segmentation},
author = {Sayan Banerjee and S Divakar Bhat and Subhasis Chaudhuri and Rajbabu Velmurugan},
journal= {arXiv preprint arXiv:2010.08800},
year = {2020}
}
Comments
Accepted at 2020 25th International Conference on Pattern Recognition (ICPR)