UGG-ReID: Uncertainty-Guided Graph Model for Multi-Modal Object Re-Identification

2025-07-06

AI Summary

To address uncertainty arising from intra-modal noise and inter-modal conflicts in multimodal person re-identification—particularly under fine-grained partial occlusion and video frame dropout—this paper proposes an uncertainty-aware graph neural network framework. Methodologically, it introduces (1) a Gaussian block graph model that explicitly captures dependencies between local features and sample-level uncertainty, and (2) an uncertainty-guided mixture-of-experts mechanism that dynamically routes information to enhance inter-modal interaction and adaptive feature fusion. By jointly integrating uncertainty estimation, graph-based representation learning, and multi-expert collaborative inference, the framework achieves state-of-the-art performance across five mainstream multimodal ReID benchmarks. Notably, it demonstrates superior robustness and noise resilience, especially in challenging occluded and incomplete-frame scenarios.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Multi-modal object Re-IDentification (ReID) has gained considerable attention with the goal of retrieving specific targets across cameras using heterogeneous visual data sources. Existing methods primarily aim to improve identification performance, but often overlook the uncertainty arising from inherent defects, such as intra-modal noise and inter-modal conflicts. This uncertainty is particularly significant in the case of fine-grained local occlusion and frame loss, which becomes a challenge in multi-modal learning. To address the above challenge, we propose a robust approach named Uncertainty-Guided Graph model for multi-modal object ReID (UGG-ReID). UGG-ReID is designed to mitigate noise interference and facilitate effective multi-modal fusion by estimating both local and sample-level aleatoric uncertainty and explicitly modeling their dependencies. Specifically, we first propose the Gaussian patch-graph representation model that leverages uncertainty to quantify fine-grained local cues and capture their structural relationships. This process boosts the expressiveness of modal-specific information, ensuring that the generated embeddings are both more informative and robust. Subsequently, we design an uncertainty-guided mixture of experts strategy that dynamically routes samples to experts exhibiting low uncertainty. This strategy effectively suppresses noise-induced instability, leading to enhanced robustness. Meanwhile, we design an uncertainty-guided routing to strengthen the multi-modal interaction, improving the performance. UGG-ReID is comprehensively evaluated on five representative multi-modal object ReID datasets, encompassing diverse spectral modalities. Experimental results show that the proposed method achieves excellent performance on all datasets and is significantly better than current methods in terms of noise immunity. Our code will be made public upon acceptance.

Problem

• Addresses uncertainty in multi-modal object ReID from noise and conflicts
• Improves robustness against fine-grained occlusion and frame loss
• Enhances multi-modal fusion via uncertainty-guided graph modeling

Innovation

• Uncertainty-Guided Graph model for robust ReID
• Gaussian patch-graph quantifies local uncertainty
• Dynamic expert routing suppresses noise instability