  The goal of Text-to-image person retrieval is to retrieve person images from
a large gallery that match the given textual descriptions. The main challenge
of this task lies in the significant differences in information representation
between the visual and textual modalities. The textual modality conveys
abstract and precise information through vocabulary and grammatical structures,
while the visual modality conveys concrete and intuitive information through
images. To fully leverage the expressive power of textual representations, it
is essential to accurately map abstract textual descriptions to specific
images.
  To address this issue, we propose a novel framework to Unleash the
Imagination of Text (UIT) in text-to-image person retrieval, aiming to fully
explore the power of words in sentences. Specifically, the framework employs
the pre-trained full CLIP model as a dual encoder for the images and texts ,
taking advantage of prior cross-modal alignment knowledge. The Text-guided
Image Restoration auxiliary task is proposed with the aim of implicitly mapping
abstract textual entities to specific image regions, facilitating alignment
between textual and visual embeddings. Additionally, we introduce a cross-modal
triplet loss tailored for handling hard samples, enhancing the model's ability
to distinguish minor differences.
  To focus the model on the key components within sentences, we propose a novel
text data augmentation technique. Our proposed methods achieve state-of-the-art
results on three popular benchmark datasets, and the source code will be made
publicly available shortly.
