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

arXiv: https://arxiv.org/abs/2507.04638 · HTML: https://mlsanity.com/paper/2507.04638/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 21 of 21)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [NExT-GPT: Any-to-Any Multimodal LLM](https://mlsanity.com/paper/2309.05519/) (2023)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Omni-Scale Feature Learning for Person Re-Identification](https://mlsanity.com/paper/1905.00953/) (2019)
- [Data Uncertainty Learning in Face Recognition](https://mlsanity.com/paper/2003.11339/) (2020)
- [Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline](https://mlsanity.com/paper/2309.14611/) (2023)
- [Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts](https://mlsanity.com/paper/2410.08245/) (2024)
- [Noisy-Correspondence Learning for Text-to-Image Person Re-identification](https://mlsanity.com/paper/2308.09911/) (2023)
- [MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model](https://mlsanity.com/paper/2210.05335/) (2022)
- [Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2403.10254/) (2024)
- [TOP-ReID: Multi-spectral Object Re-Identification with Token Permutation](https://mlsanity.com/paper/2312.09612/) (2023)
- [DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2412.10650/) (2024)
- [IDEA: Inverted Text with Cooperative Deformable Aggregation for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2503.10324/) (2025)
- [MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt](https://mlsanity.com/paper/2412.10707/) (2024)
- [UniCat: Crafting a Stronger Fusion Baseline for Multimodal Re-Identification](https://mlsanity.com/paper/2310.18812/) (2023)
- [ModaVerse: Efficiently Transforming Modalities with LLMs](https://mlsanity.com/paper/2401.06395/) (2024)
- [Multi-spectral Vehicle Re-identification with Cross-directional Consistency Network and a High-quality Benchmark](https://mlsanity.com/paper/2208.00632/) (2022)
- [Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations](https://mlsanity.com/paper/2501.11916/) (2025)
- [Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification](https://mlsanity.com/paper/2305.13659/) (2023)
- [ICPL-ReID: Identity-Conditional Prompt Learning for Multi-Spectral Object Re-Identification](https://mlsanity.com/paper/2505.17821/) (2025)
- [A Bayesian Approach to OOD Robustness in Image Classification](https://mlsanity.com/paper/2403.07277/) (2024)

## Built on by (most cited first, 4 of 4)

- [STMI: Segmentation-Guided Token Modulation with Cross-Modal Hypergraph Interaction for Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2603.00695/) (2026)
- [Beyond Visual Cues: Semantic-Driven Token Filtering and Expert Routing for Anytime Person ReID](https://mlsanity.com/paper/2604.15090/) (2026)
- [Multi-Modal Object Re-Identification with Dual Semantic Guidance and Global-Local Mutual Modulation](https://mlsanity.com/paper/2607.29207/) (2026)
- [TRIM-ReID: Duplication-Aware Token Reduction and Modality-Aligned Interaction for Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2610.04361/) (2026)

## Abstract (arXiv)

Multi-modal object Re-IDentification (ReID) has gained considerable attention with the goal of retrieving specific targets across cameras using heterogeneous visual data sources. At present, multi-modal object ReID faces two core challenges: (1) learning robust features under fine-grained local noise caused by occlusion, frame loss, and other disruptions; and (2) effectively integrating heterogeneous modalities to enhance multi-modal representation. To address the above challenges, 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 is available at https://github.com/wanxixi11/UGG-ReID.