Title: UGG-ReID: Uncertainty-Guided Graph Model for Multi-Modal Object Re-Identification
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multi-Modal Object Re-Identification

Uncertainty-Guided Graph

Gaussian Patch-Graph Representation

Mixture of Experts

Uncertainty-Guided Routing
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. 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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel framework to address the challenges of local noise and modality misalignment in multimodal ReID. The key ideas are twofold: GPGR quantifies epistemic uncertainty in local and global features and models their structural dependencies, and UGMoE dynamically selects reliable experts based on sample-level uncertainty to improve cross-modal interaction. Extensive experiments on five public multi-modal ReID datasets show that UGG-ReID outperforms strong baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper tackles a critical challenge in multi-modal Re-ID: fine-grained local noise and inter-modal inconsistencies, which degrade robustness.\n2. It achieves state-of-the-art performance in mAP and Rank-1 across all datasets, and performs especially well under artificially injected Gaussian noise, showing strong robustness and generalization. \n\nWeaknesses:\n1. There are several instances where full terms and their abbreviations (e.g., Gaussian Patch-Graph Representation (GPGR), Uncertainty-Guided Mixture of Experts (UGMoE), Mixture of Experts (MoE)) are repeatedly introduced or inconsistently used throughout different sections. This redundancy and inconsistency negatively affect readability and make it harder for readers to follow the paper's core ideas. Terms like “uncertainty-guided” and “modal-specific” are used frequently without consistent clarification. These should be explained clearly in the introduction.\n2. While the paper introduces uncertainty modeling via GPGR and UGMoE, similar concepts have already been explored in prior work. DeMo also leverages expert selection and attention mechanisms for multi-modal object Re-ID, and uncertainty-guided fusion has been studied in the broader multi-modal learning literature (e.g., MAP, PDA). Although mechanisms like Gaussian modeling, KL divergence, MoE routing are introduced, the distinction between UGG-ReID and these approaches is not clear.\n\nQUESTIONS:\nAccording to the weaknesses, my questions are:\n1. The authors should improve the paper's clarity, including full terms and their abbreviations, repeatedly introduced parts, and terms without explanations.\n2. Please provide a clearer conceptual and technical comparison to prior works. This could be done through a comparison table in the methodology section that breaks down components across methods (e.g., whether uncertainty is modeled locally vs. globally, how routing is performed, whether expert selection is dynamic, etc.). Also, consider explicitly clarifying which aspects of your framework are new and which are extensions of known paradigms.\n3. The authors are encouraged to supplement the Limitations section.\n\nLIMITATIONS:\nNo", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents UGG-ReID, a novel multimodal person re-identification framework designed to address two key challenges: (1) the suppression of local noise interference, and (2) the effective fusion of heterogeneous modality features. The proposed approach introduces two main innovations: a Gaussian Patch Graph Representation (GpGR) that models feature uncertainty through Gaussian distributions and captures structural relations via graph propagation; and an Uncertainty-Guided Mixture-of-Experts (UGMoE) strategy, which dynamically assigns samples to specialized experts based on estimated uncertainty, thereby enhancing cross-modal interaction. Extensive evaluations on five public multimodal ReID benchmarks demonstrate that UGG-ReID achieves state-of-the-art performance, particularly exhibiting superior robustness under noisy conditions. Comprehensive ablation studies and robustness analyses further validate the effectiveness of each core component.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.\tThe paper conducts comprehensive experiments across five representative multimodal ReID benchmarks, covering both pedestrian and vehicle scenarios, which demonstrates the generalizability and effectiveness of the proposed approach.\n2.\tThe work presents a novel perspective by incorporating Gaussian distribution modeling into local feature representation and leveraging sample-level uncertainty to guide cross-modal interaction, showing good originality.\n3.\tThe paper is well-structured and logically presented. The progression from problem formulation to method design, experimentation, and result analysis is clear and easy to follow.\n4.\tThe proposed method achieves strong performance, especially under noisy conditions, and outperforms state-of-the-art approaches, indicating its robustness to modality inconsistency and noise interference.\nWeaknesses:\n1.\tThe mathematical exposition of the Gaussian graph convolution and uncertainty-based routing is limited. There is no analysis of convergence, complexity, or theoretical guarantees, which reduces the theoretical depth of the work.\n2.\tThe core components—Gaussian modeling and Mixture-of-Experts—are not entirely novel, and the paper lacks sufficient discussion on their integration, boundary advantages, or potential synergy, which would have strengthened the methodological contribution.\n3.\tSome equations and notations are insufficiently explained, which could cause confusion for readers. For instance, the variable lms is introduced but never clearly utilized in the final loss formulation.\n4.\tWhile the UGMoE module is claimed to dynamically route samples to experts based on uncertainty, no visualization or statistical evidence is provided to support how samples are distributed across experts, nor is the actual benefit of such routing quantitatively analyzed.\n5.\tThe paper mentions training on a single RTX 4090 GPU but lacks details on model size or inference latency. Given the use of graph convolutions and MoE, which are generally computation-intensive, the method may face challenges in real-world deployment scenarios.\n6.\tThe noise robustness evaluation relies primarily on synthetic noise (e.g., Gaussian, arbitrary), which may not fully reflect real-world challenges such as occlusion, illumination changes, or viewpoint shifts.\n\nQUESTIONS:\nIn Fig. 3, as the magnitude of Gaussian noise increases, UGG-ReID shows a greater performance drop compared to Demo, which seems to suggest that UGG-ReID may not be more robust than Demo. Could you clarify your view on this observation?\nIn Section 3.3.1, the Uncertainty-Guided Experts Network introduces a new loss function. However, this loss does not appear to be included in the overall training objective. Could you provide an explanation for this omission?\nCould the authors elaborate on the theoretical motivation for using Gaussian distributions to model local uncertainties? Why is this choice particularly appropriate in the context of multimodal ReID?\nHave the authors considered extending UGG-ReID to other multimodal tasks such as cross-modal retrieval? If not, could the authors discuss the potential challenges or limitations that may arise in such scenarios?\nTo what extent is the performance of UGG-ReID sensitive to key hyperparameters, such as the number of experts in the UGMoE module or the construction parameters in the GpGR? A sensitivity analysis or ablation could help clarify this aspect.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-10"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper focus on the valuable task of multimodal object re-identification. The authors mainly focus on two under-explored challenges, to learn robust representations against local noise and integrate different modalities. To combat, the authors propose GPGR that estimates uncertainty through Gaussian distributions and patch-graph representations, and UGMoE that guide samples to experts of low uncertainties. Further experiments demonstrate the effectiveness of UGG-ReID.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n\n1. The targeted multimodal object re-identification task is valuable in real-world applications.\n2. The paper is well-written and easy to follow.\n3. The proposed UGG-ReID achieves significant improvements on performance than SOTAs.\n\nCons:\n\n1. The novelty of the method is limited from my perspective. The paper mainly focus on dealing with local noise, which has been  researched by several previous works, the strategies like patch-graph and uncertainty estimation have been widely applied before. Besides, a branch of related works that focus on multimodal noise [1-3] in ReID have not been discussed or compared by the authors.\n2. I appreciate that the authors try to deal with noise through MoE strategies. However, I concern about whether the balance between experts can be maintained. Although balancing loss has been applied in Eq. 12, I still worry that few experienced experts will deal with most samples with high certainty. I wonder if the authors can give visualization of router to show whether experts are balanced.\n3. The authors introduce a quite complicated framwork in this paper, including patch-wise Gaussian distribution estimation, patch-graph calculation, MoE, etc. Such framework is concerned to have high computational cost. I wonder if the authors can provide wall-clock time costs of UGG-ReID against SOTAs to prove that the method is efficient.\n\n[1] Learning With Twin Noisy Labels for Visible-Infrared Person Re-Identification\n\n[2] Robust Object Re-identification with Coupled Noisy Labels\n\n[3] Noisy-Correspondence Learning for Text-to-Image Person Re-identification\n\nQUESTIONS:\nPlease refer to the Cons section.\n\nI will consider raise my score if the authors can:\n1. explain the novelty of the method, and discuss or compare with the mentioned related works.\n2. provide visualization of routers to show different experts are balanced.\n3. provide wall-clock time costs of UGG-ReID against SOTAs.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes an Uncertainty-Guided Graph model for multi-modal object re-identification named UGG-ReID. UGG-ReID tackles two main challenges: local noise from occlusions or frame loss and effective multi-modal data fusion. It introduces Gaussian Patch-Graph Representation (GPGR) to quantify and structure epistemic uncertainty at global and local levels, enhancing feature robustness. Additionally, an Uncertainty-Guided Mixture of Experts (UGMoE) dynamically routes samples to appropriate experts based on uncertainty, improving fusion and reducing noise effects. Extensive experiments on five datasets demonstrate state-of-the-art performance and strong robustness in noisy conditions, confirming the method’s effectiveness.\n\nSTRENGTHS AND WEAKNESSES:\nThe strength of this paper lies in its performance and the idea of uncertainty. Compared to previous works, this work has achieved significant performance improvements. The weakness of this paper may lie in its unclear motivation and introduction. It makes me feel somewhat confused about why these parts need to be introduced. Meanwhile, the lack of visualization results also harms this paper. Although it is focused on uncertainty. There are no visualization results or analysis to show that these modules work in this way.\n\nQUESTIONS:\n1. The motivation and Figure 1 are somewhat confusing. How do you define the local noise and how it influenced the final retrieval performance? It will be better to give some examples or a better explanation.\n\n2. Using graph nodes is not fresh in the ReID. HOReID [1] is a typical strategy that uses ga raph network to solve the missing information. What is the advantage of this work? Meanwhile, I think it will be more specific and interesting if using the graph passing to achieve interaction between different modalities.\n\n3. In Figure 2, the R, N, and T features are not considered equally in the UGMoE. In the paper, the author seems to consider them equally. So, how does UGMoE work?\n\n4. Are there any visualization results that can be given to show the difference in the uncertainty and expert selection? I think it will be useful to demonstrate the effectiveness of each module.\n\n[1] Wang G, Yang S, Liu H, et al. High-order information matters: Learning relation and topology for occluded person re-identification[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 6449-6458.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}