Text-Based Person Search, which aims to retrieve target pedestrian images using natural language descriptions, has garnered significant attention in multimedia research due to its potential in suspect retrieval and missing person identification. While supervised and weakly supervised methods rely on costly annotated training data, unsupervised TBPS eliminates the need for textual descriptions or identity annotations, presenting a more practical paradigm. Current unsupervised TBPS approaches face two primary challenges: 1) Predefined attribute templates for caption generation limit linguistic diversity and real-world adaptability, and 2) Threshold-based sample selection using pre-trained vision-language models (VLMs) introduces noisy pairs due to inadequate pedestrian-specific representation. To address these limitations, we propose FACE, a unified framework featuring Dual-template Caption Generation (DCG) and Adaptive Curriculum Training (ACT). The DCG module generates high-quality captions through complementary flexible-style (natural language) and fixed-style (attribute-enumerated) templates, enhanced by LLM-based noise filtering. The ACT framework progressively refines training through a self-improving loop: initial high-confidence sample selection using VLMs bootstraps the model, while evolving feature representations enable dynamic incorporation of harder samples through curriculum learning. This dual strategy achieves mutual reinforcement between caption quality and model discriminability. Extensive experiments on CUHK-PEDES, ICFG-PEDES and RSTPReid datasets under unsupervised settings demonstrate that our framework achieves the state-of-the-art performance.

Unleashing the power of image-text matching in real-world applications is hampered by noisy correspondence. Manually curating high-quality datasets is expensive and time-consuming, and datasets generated using diffusion models are not adequately well-aligned. The most promising way is to collect image-text pairs from the Internet, but it will inevitably introduce noisy correspondence. To reduce the negative impact of noisy correspondence, we propose a novel model that first transforms the noisy correspondence filtering problem into a similarity distribution modeling problem by exploiting the powerful capabilities of pre-trained models. Specifically, we use the Gaussian Mixture model to model the similarity obtained by CLIP as clean distribution and noisy distribution, to filter out most of the noisy correspondence in the dataset. Afterward, we used relatively clean data to fine-tune the model. To further reduce the negative impact of unfiltered noisy correspondence, i.e., a minimal part where two distributions intersect during the fine-tuning process, we propose a distribution-sensitive dynamic margin ranking loss, further increasing the distance between the two distributions. Through continuous iteration, the noisy correspondence gradually decreases and the model performance gradually improves. Our extensive experiments demonstrate the effectiveness and robustness of our model even under high noise rates.