We present a novel and effective method calibrating cross-modal features for
text-based person search. Our method is cost-effective and can easily retrieve
specific persons with textual captions. Specifically, its architecture is only
a dual-encoder and a detachable cross-modal decoder. Without extra multi-level
branches or complex interaction modules as the neck following the backbone, our
model makes a high-speed inference only based on the dual-encoder. Besides, our
method consists of two novel losses to provide fine-grained cross-modal
features. A Sew loss takes the quality of textual captions as guidance and
aligns features between image and text modalities. A Masking Caption Modeling
(MCM) loss uses a masked captions prediction task to establish detailed and
generic relationships between textual and visual parts. We show the top results
in three popular benchmarks, including CUHK-PEDES, ICFG-PEDES, and RSTPReID. In
particular, our method achieves 73.81% Rank@1, 74.25% Rank@1 and 57.35% Rank@1
on them, respectively. In addition, we also validate each component of our
method with extensive experiments. We hope our powerful and scalable paradigm
will serve as a solid baseline and help ease future research in text-based
person search.

本文提出一种基于文本的人物搜索的新颖有效方法，使用双编码器和分离的跨模态解码器架构，采用两种新的损失来提供细粒度的跨模态特征，并在 CUHK-PEDES，ICFG-PEDES 和 RSTPReID 等三个流行基准测试中创造了排名前三的成绩。