The emergence of cross-modal foundation models has introduced numerous
approaches grounded in text-image retrieval. However, on some domain-specific
retrieval tasks, these models fail to focus on the key attributes required. To
address this issue, we propose a self-enhancement framework, A^{3}R, based on
the CLIP-ViT/G-14, one of the largest cross-modal models. First, we perform an
Attribute Augmentation strategy to enrich the textual description for
fine-grained representation before model learning. Then, we propose an Adaption
Re-ranking method to unify the representation space of textual query and
candidate images and re-rank candidate images relying on the adapted query
after model learning. The proposed framework is validated to achieve a salient
improvement over the baseline and other teams' solutions in the cross-modal
image retrieval track of the 1st foundation model challenge without introducing
any additional samples. The code is available at
https://github.com/CapricornGuang/A3R.

本篇研究提出了一个基于自我增强框架 (A^{3} R) 的跨模态图像检索方法，在领域特定任务中较传统方法表现更好，这个方法采用属性增强的策略来丰富文本描述，并且提出了一种调整重排的方法来寻找文本检索查询和候选图像的表征空间，因此实现了对基线和其他团队解决方案在不引入任何额外样本的情况下实现显著改进。