Searching persons in large-scale image databases with the query of natural
language description has important applications in video surveillance. Existing
methods mainly focused on searching persons with image-based or attribute-based
queries, which have major limitations for a practical usage. In this paper, we
study the problem of person search with natural language description. Given the
textual description of a person, the algorithm of the person search is required
to rank all the samples in the person database then retrieve the most relevant
sample corresponding to the queried description. Since there is no person
dataset or benchmark with textual description available, we collect a
large-scale person description dataset with detailed natural language
annotations and person samples from various sources, termed as CUHK Person
Description Dataset (CUHK-PEDES). A wide range of possible models and baselines
have been evaluated and compared on the person search benchmark. An Recurrent
Neural Network with Gated Neural Attention mechanism (GNA-RNN) is proposed to
establish the state-of-the art performance on person search.

本论文提出一种基于自然语言描述的人物搜索方法，利用 CUHK Person Description Dataset 建立了人物描述与样本的对应关系，并使用一种基于 Gated Neural Attention 机制的循环神经网络，实现了在人物搜索中的最优性能。