  Text attribute person search aims to find specific pedestrians through given
textual attributes, which is very meaningful in the scene of searching for
designated pedestrians through witness descriptions. The key challenge is the
significant modality gap between textual attributes and images. Previous
methods focused on achieving explicit representation and alignment through
unimodal pre-trained models. Nevertheless, the absence of inter-modality
correspondence in these models may lead to distortions in the local information
of intra-modality. Moreover, these methods only considered the alignment of
inter-modality and ignored the differences between different attribute
categories. To mitigate the above problems, we propose an Attribute-Aware
Implicit Modality Alignment (AIMA) framework to learn the correspondence of
local representations between textual attributes and images and combine global
representation matching to narrow the modality gap. Firstly, we introduce the
CLIP model as the backbone and design prompt templates to transform attribute
combinations into structured sentences. This facilitates the model's ability to
better understand and match image details. Next, we design a Masked Attribute
Prediction (MAP) module that predicts the masked attributes after the
interaction of image and masked textual attribute features through multi-modal
interaction, thereby achieving implicit local relationship alignment. Finally,
we propose an Attribute-IoU Guided Intra-Modal Contrastive (A-IoU IMC) loss,
aligning the distribution of different textual attributes in the embedding
space with their IoU distribution, achieving better semantic arrangement.
Extensive experiments on the Market-1501 Attribute, PETA, and PA100K datasets
show that the performance of our proposed method significantly surpasses the
current state-of-the-art methods.
