MABNet：基于混合学习的图像检索主助手伙伴网络
MABNet: Master Assistant Buddy Network with Hybrid Learning for Image Retrieval
摘要：
图像检索在最近一段时间已经获得了越来越多的兴趣。目前的方法是监督或自我监督。这些方法没有利用混合学习使用监督和自我监督的好处。我们提出了一种新的主助理伙伴网络（MABNet）的图像检索，它结合了两种学习机制。MABNet由主模块和辅助模块组成，两者都通过监督独立学习，并通过自我监督进行集体学习。主教练通过提供其知识库作为自我监督的参考来指导助理教练，助理教练通过权重转移将其知识报告给主教练。我们在有和没有后处理的公共数据集上进行了广泛的实验。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
