The task of searching certain people in videos has seen increasing potential
in real-world applications, such as video organization and editing. Most
existing approaches are devised to work in an offline manner, where identities
can only be inferred after an entire video is examined. This working manner
precludes such methods from being applied to online services or those
applications that require real-time responses. In this paper, we propose an
online person search framework, which can recognize people in a video on the
fly. This framework maintains a multimodal memory bank at its heart as the
basis for person recognition, and updates it dynamically with a policy obtained
by reinforcement learning. Our experiments on a large movie dataset show that
the proposed method is effective, not only achieving remarkable improvements
over online schemes but also outperforming offline methods.

本文提出了一种在线人员搜索框架，它采用多模态记忆库作为人员识别的基础，并通过强化学习获得策略进行动态更新。实验结果表明，该方法不仅实现了在线模式下的显著改进，而且胜过了离线方法。