Text-to-image person re-identification (TIReID) retrieves pedestrian images of the same identity based on a query text. However, existing methods for TIReID typically treat it as a one-to-one image-text matching problem, only focusing on the relationship between image-text pairs within a view. The many-to-many matching between image-text pairs across views under the same identity is not taken into account, which is one of the main reasons for the poor performance of existing methods. To this end, we propose a simple yet effective framework, called LCR$^2$S, for modeling many-to-many correspondences of the same identity by learning comprehensive representations for both modalities from a novel perspective. We construct a support set for each image (text) by using other images (texts) under the same identity and design a multi-head attentional fusion module to fuse the image (text) and its support set. The resulting enriched image and text features fuse information from multiple views, which are aligned to train a "richer" TIReID model with many-to-many correspondences. Since the support set is unavailable during inference, we propose to distill the knowledge learned by the "richer" model into a lightweight model for inference with a single image/text as input. The lightweight model focuses on semantic association and reasoning of multi-view information, which can generate a comprehensive representation containing multi-view information with only a single-view input to perform accurate text-to-image retrieval during inference. In particular, we use the intra-modal features and inter-modal semantic relations of the "richer" model to supervise the lightweight model to inherit its powerful capability. Extensive experiments demonstrate the effectiveness of LCR$^2$S, and it also achieves new state-of-the-art performance on three popular TIReID datasets.

翻译：文本-图像行人重识别（TIReID）旨在根据查询文本检索同一身份的行人图像。然而，现有方法通常将其视为一对一图像-文本匹配问题，仅关注单视角内图像-文本对之间的关系，忽略了同一身份下跨视角图像-文本对间的多对多匹配关系，这是导致现有方法性能欠佳的主要原因之一。为此，我们提出一种简单有效的框架LCR$^2$S，通过从新视角学习两种模态的综合表征来建模同一身份的多对多对应关系。我们利用同一身份下的其他图像（文本）为每个图像（文本）构建支持集，并设计多头注意力融合模块对图像（文本）及其支持集进行融合。由此得到的增强型图像与文本特征融合了多视角信息，并通过对齐训练具有多对多对应关系的"更丰富"TIReID模型。由于推理阶段无法获取支持集，我们提出将"更丰富"模型学到的知识蒸馏至轻量级模型，使得该模型仅以单张图像/文本作为输入即可完成推理。轻量级模型专注于多视角信息的语义关联与推理，能够仅通过单视角输入生成包含多视角信息的综合表征，在推理阶段实现精确的文本-图像检索。特别地，我们利用"更丰富"模型的模态内特征与模态间语义关系来监督轻量级模型继承其强大能力。大量实验验证了LCR$^2$S的有效性，并在三个主流TIReID数据集上取得了新的最优性能。