Pseudo Sentences Evaluation and Quality-Aware Robust Learning for Unsupervised Text-Based Person Search.

Abstract

Unsupervised Text-Based Person Search (TBPS) eliminates the need for costly manual sentence annotations by generating pseudo sentences via Multi-modal Large Language Models (MLLMs). However, these pseudo sentences often face the quality defect issues, resulting in semantic misalignment across modalities, which will hinder discriminative representation learning. To address this problem, we propose the PSE-QRL (Pseudo Sentences Evaluation and Quality-aware Robust Learning), a unified framework that enhances robustness to pseudo sentences for unsupervised TBPS. The PSE-QRL dynamically couples an evolving TBPS model with MLLMs to assess pseudo sentences' reliability, and adaptively leverages high-quality ones during training. It consists of three key components: 1) Multi-granularity Sentence Augmentation, for enriching pseudo sentences with multiple granularities to broaden the diversity of image-sentence pairs; 2) Hybrid Quality Evaluation, to combine MLLM's cross-modal reasoning knowledge with TBPS model's person-specific distinguishing capabilities for effective sentence quality assessment; and 3) Quality-aware Robust Learning, for selecting and re-weighting samples based on quality scores to emphasize reliable sentence annotations while suppressing low-quality ones. Extensive experiments on CUHK-PEDES, ICFG-PEDES, and RSTPReid benchmarks demonstrate the effectiveness of PSE-QRL for improving learning robustness, achieving state-of-the-art (SOTA) retrieval performance for unsupervised TBPS.