  In this work, we consider the problem of searching people in an unconstrained
environment, with natural language descriptions. Specifically, we study how to
systematically design an algorithm to effectively acquire descriptions from
humans. An algorithm is proposed by adapting models, used for visual and
language understanding, to search a person of interest (POI) in a principled
way, achieving promising results without the need to re-design another
complicated model. We then investigate an iterative question-answering (QA)
strategy that enable robots to request additional information about the POI's
appearance from the user. To this end, we introduce a greedy algorithm to rank
questions in terms of their significance, and equip the algorithm with the
capability to dynamically adjust the length of human-robot interaction
according to model's uncertainty. Our approach is validated not only on
benchmark datasets but on a mobile robot, moving in a dynamic and crowded
environment.
