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Text-based person search aims to simultaneously localize and identify thetarget person based on query text from uncropped scene images, which can beregarded as the unified task of person detection and text-based personretrieval task. In this work, we propose a large-scale benchmark dataset namedPRW-TPS-CN based on the widely used person search dataset PRW. Our datasetcontains 47,102 sentences, which means there is quite more information thanexisting dataset. These texts precisely describe the person images from top tobottom, which in line with the natural description order. We also provide bothChinese and English descriptions in our dataset for more comprehensiveevaluation. These characteristics make our dataset more applicable. Toalleviate the inconsistency between person detection and text-based personretrieval, we take advantage of the rich texts in PRW-TPS-CN dataset. Wepropose to aggregate multiple texts as text prototypes to maintain theprominent text features of a person, which can better reflect the wholecharacter of a person. The overall prototypes lead to generating the imageattention map to eliminate the detection misalignment causing the decrease oftext-based person retrieval. Thus, the inconsistency between person detectionand text-based person retrieval is largely alleviated. We conduct extensiveexperiments on the PRW-TPS-CN dataset. The experimental results show thePRW-TPS-CN dataset's effectiveness and the state-of-the-art performance of ourapproach.

Prototype-Guided Text-based Person Search based on Rich Chinese Descriptions