  Searching for specific person has great social benefits and security value,
and it often involves a combination of visual and textual information.
Conventional person retrieval methods, whether image-based or text-based,
usually fall short in effectively harnessing both types of information, leading
to the loss of accuracy. In this paper, a whole new task called Composed Person
Retrieval (CPR) is proposed to jointly utilize both image and text information
for target person retrieval. However, the supervised CPR requires very costly
manual annotation dataset, while there are currently no available resources. To
mitigate this issue, we firstly introduce the Zero-shot Composed Person
Retrieval (ZS-CPR), which leverages existing domain-related data to resolve the
CPR problem without expensive annotations. Secondly, to learn ZS-CPR model, we
propose a two-stage learning framework, Word4Per, where a lightweight Textual
Inversion Network (TINet) and a text-based person retrieval model based on
fine-tuned Contrastive Language-Image Pre-training (CLIP) network are learned
without utilizing any CPR data. Thirdly, a finely annotated Image-Text Composed
Person Retrieval (ITCPR) dataset is built as the benchmark to assess the
performance of the proposed Word4Per framework. Extensive experiments under
both Rank-1 and mAP demonstrate the effectiveness of Word4Per for the ZS-CPR
task, surpassing the comparative methods by over 10\%. The code and ITCPR
dataset will be publicly available at
https://github.com/Delong-liu-bupt/Word4Per.
