Dual-Edged Homogeneous-Modality Similarity: Towards Visible-Infrared Modality-Incomplete Person Re-Identification with Modality Adaptive Matching

2026-07-21 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

Visible-Infrared Modality-Incomplete Re-Identification (VIMI-ReID) addresses the limitations of traditional Visible-Infrared Person Re-Identification (VI-ReID) in open-world scenarios. Unlike VI-ReID's closed-world assumption of heterogeneous modalities, VIMI-ReID accounts for queries and galleries potentially containing visible-only, infrared-only, or mixed-modality images. Existing VI-ReID methods struggle with matching conflicts, modality uncertainty, and robustness degradation under these diverse conditions. To tackle these challenges, the authors formalize VIMI-ReID, creating SYSU-VIMI and RegDB-VIMI benchmarks. They propose the Modality Adaptive Matching Transformer (MAMT), which employs a Divergence Transformer Module (DTM) for modality-specific features and a Shared Transformer Module (STM) for modality-shared features. Guided by a divergence loss, DTM enhances discriminability. A Modality Adaptive Matching Module (MAM) dynamically fuses features based on query-gallery modality relationships, ensuring stable matching under arbitrary and uncertain modality conditions. Experiments on VIMI benchmarks confirm MAMT's effectiveness and adaptability.

Key takeaway

For Computer Vision Engineers deploying person re-identification systems in open-world scenarios, your existing Visible-Infrared Re-ID (VI-ReID) methods will likely fail due to unpredictable modality combinations and homogeneous-modality similarity. You should adopt the new Visible-Infrared Modality-Incomplete Re-Identification (VIMI-ReID) task framework. Implement solutions like the Modality Adaptive Matching Transformer (MAMT) to ensure reliable, consistent, and dynamically adaptable visual recognition across diverse visible and infrared inputs. This approach directly addresses real-world modality uncertainty.

Key insights

VIMI-ReID addresses open-world person re-identification by handling diverse visible-infrared modality combinations with adaptive matching.

Principles

• Open-world Re-ID requires handling homogeneous and heterogeneous modalities.
• Modality-specific and shared features improve discriminability.
• Dynamic feature fusion adapts to query-gallery modality relationships.

Method

The Modality Adaptive Matching Transformer (MAMT) uses a Divergence Transformer Module (DTM) for modality-specific features and a Shared Transformer Module (STM) for shared features, guided by divergence loss, then dynamically fuses them with a Modality Adaptive Matching Module (MAM).

In practice

• Reorganize datasets for VIMI-ReID benchmarks like SYSU-VIMI.
• Implement DTM and STM for feature extraction.
• Apply MAM for dynamic feature fusion in multi-modal matching.

Topics

• Visible-Infrared Re-Identification
• Modality-Incomplete Re-ID
• Modality Adaptive Matching Transformer
• Divergence Transformer Module
• Person Re-Identification
• Multi-modal Learning

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 Takara TLDR - Daily AI Papers.