Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification

2026-07-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, quick

Summary

This survey, titled "Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification," provides a comprehensive overview of the transition in person re-identification (ReID) from single-modal RGB imagery to cross-modal and multi-modal paradigms. Traditional ReID methods face limitations from low illumination and occlusion. The paper systematically reviews key cross-modal tasks, including visible-infrared (VI-ReID), text-image (TI-ReID), sketch-based (Sketch-ReID), and Non-Line-of-Sight (NLOS) ReID, which extends perception beyond direct visibility. It also examines tri-spectral and multi-modal fusion ReID, highlighting how diverse sensor information enhances robustness. Beyond summarizing datasets and methodologies, the survey proposes a Transformer-based baseline framework specifically for visible-infrared ReID, designed to capture modality-invariant features. Finally, it outlines several promising future research directions in the field.

Key takeaway

For Computer Vision Engineers designing next-generation surveillance systems, you should prioritize integrating multi-modal data streams to overcome traditional RGB limitations. Consider visible-infrared, text-image, or Non-Line-of-Sight ReID to enhance robustness against low illumination and occlusion. Adopting a Transformer-based architecture for modality-invariant feature extraction will improve cross-modal matching accuracy in diverse environments. This shift is crucial for deploying reliable identity matching solutions.

Key insights

Person Re-identification is transitioning to multi-modal and cross-modal approaches to enhance robustness against environmental challenges like low illumination and occlusion.

Principles

• Diverse sensor information enhances robustness.
• Modality-invariant features improve cross-modal matching.

Method

A Transformer-based baseline framework is proposed for visible-infrared ReID, designed to effectively capture modality-invariant features for improved matching.

In practice

• Implement VI-ReID for low-light conditions.
• Explore TI-ReID for text-based queries.
• Utilize NLOS ReID for obscured subjects.

Topics

• Person Re-identification
• Multi-modal ReID
• Cross-modal ReID
• Visible-Infrared ReID
• Transformer Networks
• Surveillance Systems

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

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