Text-to-image person retrieval aims to identify the target person based on a
given textual description query. The primary challenge is to learn the mapping
of visual and textual modalities into a common latent space. Prior works have
attempted to address this challenge by leveraging separately pre-trained
unimodal models to extract visual and textual features. However, these
approaches lack the necessary underlying alignment capabilities required to
match multimodal data effectively. Besides, these works use prior information
to explore explicit part alignments, which may lead to the distortion of
intra-modality information. To alleviate these issues, we present IRRA: a
cross-modal Implicit Relation Reasoning and Aligning framework that learns
relations between local visual-textual tokens and enhances global image-text
matching without requiring additional prior supervision. Specifically, we first
design an Implicit Relation Reasoning module in a masked language modeling
paradigm. This achieves cross-modal interaction by integrating the visual cues
into the textual tokens with a cross-modal multimodal interaction encoder.
Secondly, to globally align the visual and textual embeddings, Similarity
Distribution Matching is proposed to minimize the KL divergence between
image-text similarity distributions and the normalized label matching
distributions. The proposed method achieves new state-of-the-art results on all
three public datasets, with a notable margin of about 3%-9% for Rank-1 accuracy
compared to prior methods.

研究了跨模态对齐的图像检索问题，提出了一种 IRRA 框架，并在三个公共数据集上获得了优于现有方法的最新成果。