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

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Text-Based Person Retrieval (TBPR) is crucial for intelligent surveillance.
• Existing TBPR methods struggle with cross-modal semantic inconsistency and discriminability.
• Current approaches often require larger datasets, limiting scalability.

Purpose of the Study:

• To propose a novel framework, Diversity Semantic Embedding Expansion (DSEE), for semantically enhanced representation learning in TBPR.
• To address the limitations of cross-modal semantic inconsistency and discriminability.
• To establish identity-centric cross-modal consistency through contrastive learning and generative synergy.

Main Methods:

• Developed the DSEE framework with two key modules: Bidirectional-guided Semantic Modeling (BSM) and Generative-driven Semantic Enhancement (GSE).
• BSM models similarity-based interactions between image and text modalities, emphasizing identity-level similarity for enriched representations.
• GSE uses a generative text augmentation scheme and a dual-path attention mechanism for semantic diversity and precision.

Main Results:

• DSEE achieved state-of-the-art performance on major benchmarks across diverse scenarios.
• The framework demonstrated enhanced semantic expressiveness and cross-modal alignment.
• Identity-centric consistency and enriched semantic diversity were successfully established.

Conclusions:

• DSEE provides an effective paradigm for advancing TBPR applications in real-world intelligent surveillance.
• The proposed method overcomes fundamental challenges in cross-modal semantic understanding.
• DSEE offers a scalable solution without relying on larger datasets.