Toward Semantically Enhanced Representation Learning for Text-Based Person Retrieval.

Abstract

Text-Based Person Retrieval (TBPR), which is a pivotal technology in the intelligent surveillance field, is aimed at retrieving target pedestrians based on free-form textual descriptions. While the existing methods attempt to align cross-modal features via multigranular interactions, their performance remains fundamentally limited by two core challenges: cross-modal semantic inconsistency and cross-modal semantic discriminability. To address these issues, we propose DSEE (Diversity Semantic Embedding Expansion), a novel framework for semantically enhanced representation learning. Unlike approaches that rely on constructing larger or more detailed datasets, DSEE establishes identity-centric cross-modal consistency through contrastive learning and generative synergy. The framework consists of two key modules: Bidirectional-guided Semantic Modeling (BSM) and Generative-driven Semantic Enhancement (GSE) modules. The BSM module constructs novel semantic embeddings by modeling similarity-based interactions between the image and text modalities. Specifically, it emphasizes identity-level similarity to guide the generation of enriched, discriminative semantic representations, thereby enhancing their semantic expressiveness and cross-modal alignment. The GSE module provides enriched semantic diversity through a generative text augmentation scheme based on visual inputs, while refining the semantic precision of the method via a dual-path attention mechanism that performs both intramodal refinement and cross-modal alignment. Extensive experiments demonstrate that DSEE achieves state-of-the-art performance on major benchmarks across diverse scenarios. Our work provides an effective paradigm for advancing TBPR applications in real-world settings.