Mabnet: Master Assistant Buddy Network With Hybrid Learning for Image Retrieval
Rohit Agarwal, Gyanendra Das, Saksham Aggarwal, Alexander Horsch, Dilip K. Prasad
Abstract
Image retrieval has garnered a 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 (MAB-Net) for image retrieval which incorporates both the learning mechanisms. MABNet consists of master and assistant block, 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 the public datasets with and without post-processing.
BibTeX
@inproceedings{icassp2023_mabnetmasterassi,
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},
booktitle = {ICASSP 2023},
year = {2023}
}