Abstract
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.
Abstract (translated)
图像检索最近吸引了越来越多的关注。当前的方法要么受监督,要么自监督。这些方法并没有利用同时使用监督和自监督的混合学习的好处。我们提出了一种图像检索的新型 Master AssistantBuddy Network (MABNet),它结合了监督和自监督两个学习机制。MABNet由Master和Assistant块组成,它们通过监督单独学习,并通过自监督共同学习。 Master指导Assistant,通过提供其知识库作为自监督的参考,Assistant将知识反馈给Master通过权重转移。我们在不同的公共数据集上进行了广泛的实验,包括经过预处理和未经过预处理的数据。
URL
https://arxiv.org/abs/2303.03050
