MABNet: Master Assistant Buddy Network with Hybrid Learning for Image Retrieval
This work addresses image retrieval for computer vision applications, but it appears incremental as it builds on existing supervised and self-supervised methods.
The paper tackled the problem of image retrieval by proposing MABNet, a hybrid learning network that combines supervised and self-supervised mechanisms, achieving improved performance on public datasets.
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.