  Text-based person retrieval aims to find the query person based on a textual
description. The key is to learn a common latent space mapping between
visual-textual modalities. To achieve this goal, existing works employ
segmentation to obtain explicitly cross-modal alignments or utilize attention
to explore salient alignments. These methods have two shortcomings: 1) Labeling
cross-modal alignments are time-consuming. 2) Attention methods can explore
salient cross-modal alignments but may ignore some subtle and valuable pairs.
To relieve these issues, we introduce an Implicit Visual-Textual (IVT)
framework for text-based person retrieval. Different from previous models, IVT
utilizes a single network to learn representation for both modalities, which
contributes to the visual-textual interaction. To explore the fine-grained
alignment, we further propose two implicit semantic alignment paradigms:
multi-level alignment (MLA) and bidirectional mask modeling (BMM). The MLA
module explores finer matching at sentence, phrase, and word levels, while the
BMM module aims to mine \textbf{more} semantic alignments between visual and
textual modalities. Extensive experiments are carried out to evaluate the
proposed IVT on public datasets, i.e., CUHK-PEDES, RSTPReID, and ICFG-PEDES.
Even without explicit body part alignment, our approach still achieves
state-of-the-art performance. Code is available at:
https://github.com/TencentYoutuResearch/PersonRetrieval-IVT.
