Beyond Visual Cues: Semantic-Driven Token Filtering and Expert Routing for Anytime Person ReID

2026-04-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

The Semantic-driven Token Filtering and Expert Routing (STFER) framework addresses Any-Time Person Re-identification (AT-ReID) challenges, specifically modality shifts (day/night) and extensive clothing changes. Existing methods, reliant on visual features, suffer performance degradation under these conditions. STFER leverages Large Vision-Language Models (LVLMs) to generate identity-consistent text, providing features robust to clothing variations and cross-modality shifts between RGB and IR. The framework guides LVLMs with instructions to produce identity-intrinsic semantic text, which then drives Semantic-driven Visual Token Filtering (SVTF) to enhance informative visual regions and suppress background noise. Additionally, this text token is used for Semantic-driven Expert Routing (SER), integrating semantic information for more robust multi-scenario gating. Experiments on the AT-USTC dataset show STFER achieves state-of-the-art results and demonstrates superior generalization across 5 other widely-used ReID benchmarks.

Key takeaway

For research scientists developing person re-identification systems, STFER offers a robust approach to overcome challenges posed by modality shifts and clothing changes. You should consider integrating Large Vision-Language Models to generate identity-consistent semantic text, which can significantly enhance feature discrimination and improve generalization capabilities across diverse datasets. This method provides a clear path to more reliable AT-ReID performance.

Key insights

Semantic text from LVLMs enhances person re-identification robustness against visual changes.

Principles

• Biometric constants improve identity discrimination.
• Semantic guidance filters visual noise effectively.

Method

STFER uses LVLMs to generate identity-consistent text, which then performs Semantic-driven Visual Token Filtering and Semantic-driven Expert Routing for robust person re-identification.

In practice

• Employ LVLMs for identity-discriminative text generation.
• Integrate semantic text for visual token filtering.
• Use semantic text for expert routing in multi-scenario tasks.

Topics

• Any-Time Person Re-identification
• Large Vision-Language Models
• Semantic-driven Token Filtering
• Semantic-driven Expert Routing
• Cross-Modality Re-identification

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.