Image retrieval has garnered growing interest in recent times. The current approaches are either supervised or self-supervised. These methods do not exploit the benefits of hybrid learning using both supervision and self-supervision. We present a novel Master Assistant Buddy Network (MABNet) for image retrieval which incorporates both learning mechanisms. MABNet consists of master and assistant blocks, both learning independently through supervision and collectively via self-supervision. The master guides the assistant by providing its knowledge base as a reference for self-supervision and the assistant reports its knowledge back to the master by weight transfer. We perform extensive experiments on public datasets with and without post-processing.
@article{arxiv.2303.03050,
title = {MABNet: Master Assistant Buddy Network with Hybrid Learning for Image Retrieval},
author = {Rohit Agarwal and Gyanendra Das and Saksham Aggarwal and Alexander Horsch and Dilip K. Prasad},
journal= {arXiv preprint arXiv:2303.03050},
year = {2023}
}
Comments
Accepted at International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2023