Abstract
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 (GNARNN) is proposed to establish the state-of-the art performance on person search.