  Text-based person search aims to simultaneously localize and identify the
target person based on query text from uncropped scene images, which can be
regarded as the unified task of person detection and text-based person
retrieval task. In this work, we propose a large-scale benchmark dataset named
PRW-TPS-CN based on the widely used person search dataset PRW. Our dataset
contains 47,102 sentences, which means there is quite more information than
existing dataset. These texts precisely describe the person images from top to
bottom, which in line with the natural description order. We also provide both
Chinese and English descriptions in our dataset for more comprehensive
evaluation. These characteristics make our dataset more applicable. To
alleviate the inconsistency between person detection and text-based person
retrieval, we take advantage of the rich texts in PRW-TPS-CN dataset. We
propose to aggregate multiple texts as text prototypes to maintain the
prominent text features of a person, which can better reflect the whole
character of a person. The overall prototypes lead to generating the image
attention map to eliminate the detection misalignment causing the decrease of
text-based person retrieval. Thus, the inconsistency between person detection
and text-based person retrieval is largely alleviated. We conduct extensive
experiments on the PRW-TPS-CN dataset. The experimental results show the
PRW-TPS-CN dataset's effectiveness and the state-of-the-art performance of our
approach.
