基于交互式自然语言的人员搜索
Interactive Natural Language-based Person Search
AI总结：
本文提出了一种基于交互式自然语言的人员搜索方法，通过迭代问答策略和贪心算法实现高效的目标人员搜索。
摘要：
在本工作中，我们考虑在非约束环境中通过自然语言描述进行人员搜索的问题。具体来说，我们研究如何系统地设计算法以有效获取人类的描述。通过将用于视觉和语言理解的模型进行适应，提出了一种算法，以系统的方式搜索目标人员（POI），在无需重新设计复杂模型的情况下取得了有希望的结果。随后，我们探讨了一种迭代问答（QA）策略，使机器人能够从用户处请求有关POI外貌的额外信息。为此，我们引入了一个贪心算法来按重要性对问题进行排序，并使算法能够根据模型的不确定性动态调整人机交互的长度。我们的方法不仅在基准数据集上得到验证，还在移动机器人上进行了测试，该机器人在动态且拥挤的环境中移动。
英文摘要：
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.
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。
