Text-based person search (TBPS) aims at retrieving a target person from an
image gallery with a descriptive text query. Solving such a fine-grained
cross-modal retrieval task is challenging, which is further hampered by the
lack of large-scale datasets. In this paper, we present a framework with two
novel components to handle the problems brought by limited data. Firstly, to
fully utilize the existing small-scale benchmarking datasets for more
discriminative feature learning, we introduce a cross-modal momentum
contrastive learning framework to enrich the training data for a given
mini-batch. Secondly, we propose to transfer knowledge learned from existing
coarse-grained large-scale datasets containing image-text pairs from
drastically different problem domains to compensate for the lack of TBPS
training data. A transfer learning method is designed so that useful
information can be transferred despite the large domain gap. Armed with these
components, our method achieves new state of the art on the CUHK-PEDES dataset
with significant improvements over the prior art in terms of Rank-1 and mAP.
Our code is available at this https URL

本文提出了一种框架，利用跨模态动量对比学习和转移学习方法，来解决限定数据所带来的问题，进一步提高了图像库中针对性描述查询的效率，并在 CUHK-PEDES 数据集上达到了新的最高表现。

基于文本的有限数据人物搜索

Text-Based Person Search with Limited Data

Text-based person search aims to retrieve the corresponding person images in
an image database by virtue of a describing sentence about the person, which
poses great potential for various applications such as video surveillance.
Extracting visual contents corresponding to the human description is the key to
this cross-modal matching problem. Moreover, correlated images and descriptions
involve different granularities of semantic relevance, which is usually ignored
in previous methods. To exploit the multilevel corresponding visual contents,
we propose a pose-guided multi-granularity attention network (PMA). Firstly, we
propose a coarse alignment network (CA) to select the related image regions to
the global description by a similarity-based attention. To further capture the
phrase-related visual body part, a fine-grained alignment network (FA) is
proposed, which employs pose information to learn latent semantic alignment
between visual body part and textual noun phrase. To verify the effectiveness
of our model, we perform extensive experiments on the CUHK Person Description
Dataset (CUHK-PEDES) which is currently the only available dataset for
text-based person search. Experimental results show that our approach
outperforms the state-of-the-art methods by 15 \% in terms of the top-1 metric.

该研究提出了一种基于文本描述搜索人物图像的方法，通过使用多层次的视觉内容对不同语义相关性的相关图像和描述进行匹配，其中包括了多粒度的视觉信息。在 CUHK-PEDES 数据集上进行的实验表明，该方法在 top-1 指标上比现有方法高出 15％。