UGG-ReID: Uncertainty-Guided Graph Model for Multi-Modal Object Re-Identification
Authors: Xixi Wan
AIHUA ZHENG
Bo Jiang
Beibei Wang
Chenglong Li
Jin Tang
Venue: NeurIPS 2025 poster ()
Keywords: Multi-Modal Object Re-Identification
Uncertainty-Guided Graph
Gaussian Patch-Graph Representation
Mixture of Experts
Uncertainty-Guided Routing
Primary area: deep_learning
TL;DR: We propose the  Uncertainty-Guided Graph model for multi-modal object ReID (UGG-ReID) framework for multi-modal object re-identification to effectively alleviate the interference of noisy data.

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.

UGG-ReID: Uncerfainty-Guided Graph Model for Multi-Modal Object Re-Identification
Xixi Wan' Aihua Zheng' Bo Jiang Beibei Wang Chenglong Li Jin Tang²
IInformation Materials and Intelligent Sensing Laboratory of Anhui Province,
School of Artificial Intelligence. Anhui University, Hefei, China 2 Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China 'ahzheng21 40f oxmail com, j jjangbo@ahu. edwaa
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 Rell) 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 chal- lenges, we propose a robust approach named Uncertainty-Guide Graph model for multi-modal object RelD (UGG-RelD). UGG-ReID is designed mitigate noise interference and facilitate effective multi-modal fusion by estimating both local and sample-level aleatoric uncertainty and explicitly modeling their dependen- cies. Specifically, we first propose the Gaussian patch-grapp representation model that leverages uncertainty quantify ane-grained local cues and capture their structural relationships. This process boosts the expressiveness of modal-specins information, ensuring that the generated embeddings are both more informative and robust. Subsequently, we design an uncertainty-guides 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 imcertainty-guider routing to strengthen the multi-modal interaction, improving the performance. UGG-ReID is com- prehensively evaluated on five representative multi- object ReID datasets, encompassing diverse spectral modalities. Experimental results show that the proposed method achieves excellent performance on all datasets and significantly better than current methods in terms of noise immunity. Our code is available at https://gthuu..com/vaiiiiil
Introduction
Multi-moda data has emerged as a significant trend artificial intelligence 44.. Especially driven by large model technology, more and more applications have begun to utilize multi-modal information for comprehensive analysis [5H7]. Multi-modal object Re-IDentification (ReID) [TI as cutting- edge direction of this research, not only broadens the application boundaries of traditional object Rell 14), but also effectively makes up for some limitations in cross-modal object RelD 15)
Recently, researchers have extensively explored multi-modal feature fusion and matching strategies to bridge the representation gap between different modalities for object ReID [8-20 For example,
"Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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RRG Local Noise
Samples Norse
Uncertainty-based Modeling
Uncertainty-hasel Joint Rep.
N
Sumpting
Uiserr.
Multi-modal indaal Interaction
Samples Uneer.
Renompling
(c)
Figure 1: Modeling aleatoric uncertainty in multi-modal ReID. (a) The challenges in multi-modal object RelD. (b) Local uncertainty modeling for modality-specifie refinement. (c) Joint uncertainty modeling in samples and modalities for improved fusion.
Zheng al. 19 propose a progressive fusion method to achieve effective fusion of multi- modal data. Wang et al. [8 propose HTT that exploits the relationship on unseen test data between heterogeneous modalities improve performance. On the other hand, existing methods 241 have begun to pay attention to the effect of modal noise on the discriminative properties of local regions, such as the introduction of local region alignment, noise suppression module or sample reconstruction, to enhance the robustness and improve the performance of the model. Among these, Zhang et al. [22] propose EDITOR suppress interference from background information and promote feature learn. Wang et al. (33 propose utilizing CDA to focus on localized regions of the discriminatory. We can observe that some methods usually assume that the quality and representation of each modal data balanced and stable, and ignore the local and sample noise disturbances within the modality caused by factors such as occlusion. low resolution p9] [8]. Although existing works [22 23 (25 have achieved some success in mitigating the impact of noise on network performance. there are still significant shortcomings in their approaches when confronted with inconsistent local noise patterns and samples with different noise intensities. As depicted in Fig. (a), the lack of fine- ene-ggrined local noise learn and sample-level noise handling compromises the robustness of multi-modal fusion, which increases multi-modal uncertainty and impairs overall model performance.
Therefore, to solve the above problems, we provide robust approach named Uncertainty-Guided Graph model for multi-modal object RelD (UGG-ReID). The proposed method quantifies local aleatoric uncertainty and models their structural dependencies. Following this, leveraging per-sample uncertainty guides feature refinement, promoting more reliable modality interaction. To be specific, we first propose the Gaussian local features to conform to Gaussian distributions. Meanwhile, a Gaussian patch graph is constructed to explicitly model the dependencies among these local features, thereby capturing fine-grained consistency information. As illustrated in Fig. (b). the uncertainties of local tokens are effectively modeling. Second, we design an Uncertainty-Gunt -uidded
Graphh Representation (GPGR), which encourages fine- ane-grained
evaluated and explored via uncertainty-bott Mixture of Experts (UGMoB) strategy, which dynamically assigns samples to different experts based on their uncertainty levels. Besides, this strategy incorporates an uncertainty-guider routing mechanism to enhance multi-modal interaction. As shown in Fig. (c). multi-modal data is jointly learned to improve the overall performance based on uncertainty. The entire framework is trained in an end-to-end manner. Through extensive experimentation datasets, our method not only achieves competitive performance but also consistently outperforms prior methods in noisy scenarios, validating its effectiveness and robustness.
five public
object ReID
In summary, the key contributions of this method are outlined as follows:
We propose the Iinertainty-Guudde Graph model for multi-modal object ReID (UGG-ReID). UGG-RelD first enhances features within each modality by considering the distribution of global and local cues and then leveraging experts' interaction to jointly capture complemen- tary information among multi-modal data, improving model robustness and performance.
We design a Gaussian Patch-Graph Representation (GPGR) to quantify aleatoric uncertain- ties for global and local features while modeling their relationships. GPGR can further alleviate the impact of noisy data and effectively reinforce modal-specific information. To our knowledge, this is the first work that leverages uncertainty to quantify fine-grained local details and explicitly model their dependencies in multi-modal data.

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We introduce the Uncertainty Guided Mixture of Experts (UGMoE) strategy, which makes different samples select experts based on the uncertainty and also utilizes an uncertainty- guided routing mechanism to strengthen the interaction between multi-modal features, effectively promoting modal collaboration.
2 Related Works
2.1
Multi-Modal Object RelD
The existing multi-modal ReID methods can be summarized into two types: one focuses on feature fusion and integration between modalities, aiming to alleviate the semantic representation differences between different modalities [19 224 (28 (29]. For instance, Yang et al. [29] propose the tri-interaction enhancement network (TIENet). This method applies spatial-frequency interaction enhance feature extraction and multi-modal fusion. Wang et al. [19] propose novel method called Mambalro, which utilizes mamba aggregation fuse the information of multi-modal Object ReID. Zhang et al. [24] propose PromptMA to establish effective connections among different multi-mode information. The other type focuses on the local noise interference within the modality and improves the overall recognition performance by enhancing the local region discrimination [10 21 Zheng et al. [TO] proposes CCNet, which seeks to simultaneously mitigate identification uncertainty due to modal variability and sample appearance changes by jointly modeling multi-modal heterogeneity and intraclass perturbations under view angle and lighting changes. Zhang et al. [22] propose EDITOR that uses the effects of background interference. Wang et al. ( propose the Inverted text with cooperative DEformable Aggregation (IDEA) framework to solve noise interference, enhancing feature robustness. However, the existing methods generally lack explicit modeling of fine- ane-grained local information quality and sample-leve uncertainty, which makes it difficult for the model to effectively perceive and suppress local noise in the face of complex modal degradation or multi-source noise interference, which limits its robustness and generalization ability.
Token Selection (SFTS) module to select diverse tokens and suppress
2.2
Uncertainty in Multi-Modal Learning
learning faces the challenge of uncertainty from the data layer and the model layer 30
The former is embodied in aleatoric uncertainty, which arises from indelible perceived noise: The latter manifests as an epistemic uncertainty due to the limitations of the model's capabilities. In practice, aleatoric uncertainty is common and has direct impact model performance. To this end, many works in recent years have focused on the introduction of uncertainty mechanisms to multi-modal learning effectively identify and suppress unreliable information, improving the performance of the model. Ji et al. (30) propose the Probability Distribution Encoder (PDE) module to aggregate all modalities into a probability distribution, framing the uncertainty and optimizing multi-modal representations. Gao et al. [ propose quantifying the intrinsic aleatoric uncertainty of single modality enhance multi-modal features. Zhang et al. [34] propose UMLMC that employs uncertainty-guide guided meta- meta-learning to mitigate feature- bias. Therefore, reasonable modeling and quantification of the uncertainty in multi-modal data can not only improve the robustness and generalization ability of multi-modal models but also provide more reliable decision support for practical applications.
35
3 Methodology
We propose a novel Uncertainty-Guided multi-modal object ReID (UGG-ReID) framework, as shown in Fig. 2 Specifically, the proposed UGG-ReID first designs the Gaussian Patch-Graph Representation (GPGR) model Fig. 2 (a), which quantifies both global and local uncertainties and models the dependencies between them. This enables richer feature representations for each modality and yields semantically more consistent embeddings. Subsequently, the Uncertainty-Guut Guided Mixture of Experts (UGMoE) strategy makes samples select experts of low uncertainty, promoting multi-modal interactions while mitigating the propagation of excessive noise. Overall, this framework injects a controlled amount of sample noise during the learning process and effectively captures multi-modal information, thereby enhancing the model's robustness and performance. Below, we will introduce each component of UGG-RelD with details.
3

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NIR
Uncertainty- Guided Gruph Model for Multi-Modal Object (UGG-ReID)
GeNaL
Eqett
Guley
XGB TIR Exprss
RLd bapera
SIR Expers
Sampling
Gatty
FIK Poperts
(o) Gaussion Patch-Graph Representation (GPGR)
(b) Uncertainty Guided Mixture of Experts (UGMoE)
Un-Exe
Un-Espert
Gates
PC
Un-
Gazen
TOP
EPOCE
Eya
GPGG
NI
7
Gatey
Un-Erra-
Sampling
Un Eyg-
Resmmpling
Pooling Layer
Lineor Layer
ReLU Activation
Summation
Concatenation
Softmax Activation
Goussion Distribution
GPGL
Ganssion Patch-Graph Lewming
GPGCN
Goussiaa Patch Graph Convolutionll Network
X GPGCN Liyers
Figure 2: The overall framework of the proposed Uncertainty-Guitn Guided multi-modal object ReID (UGG-ReID), which is composed of two main components: Gaussian Patch-Grie Graph Representation (GPGR) and Uncertainty- Guided Mixture of Experts (UGMoE).
3.1 Feature Initialization
To align with prior research (36
we adopt the visual encoder of CLIP with shared backbone to
extract initial features x" = {-²" T
of multi-modal data. Here, m E {R, N, T indexes the RGB, Infraee (NIR), and Thermal Infrared (TIR) modalities, while r. and x = {xT" ; 2²' ... ...
denote class tokens and local tokens, respectively. the number of local tokens.
3.2 Gaussian Patch-Graph Representation
The proposed Gaussian Patch-Graph Representation (GPGR) model aims to adopt Gaussian distri- butions as node representations and then conduct message passing between Gaussian distributions. GPGR mainly contains two key components: Gaussian Patch-Graph Learning for relationship model- ing and Gaussian Patch-Graph Convolutional Network for message passing. Next, we will introduce the above two modules in detail.
3.2.1 Gaussian Patch-Graph Learning
In this section, we construct Gaussian Patch-Graph Learning (GPGL) based the extracted initial features to capture context-aware dependencies. Let G(V", ETx) Gaussian patch-graph, V denotes the node set of the m-th modality and ETd represents the corresponding edge set. Different from deterministic learning, we assume that node representation follows a Gaussian distribution NM,, o²) to distinguish between regions with sufficient and insufficient cues and enhance the ability to learn complex localizations. To be specific, given the initial global-local feature set x" { .21"" .2. =m)) R where N
and D are the number of nodes and the dimension of each node. We first obtain the mean and standard deviation for global and local nodes
as follows,
i= ... Fe" = (x.)) " = FC, of = FC, (x?"),
o." = (x"),
i 1=1....
where FC, and FC, projection layers to learn the mean and standard deviation, respec- tively. We denote the means and variances by nrth
{peT ,,,,""" ........""""" E NND and oT ...".."""""" NDD respectively. Then, we calculate the edge weight in EM and construct the structural relationship achieve global and local information interaction. Let a be the adjacency matrix and encode structural relationships of m-th modality. Specifically, we compute the similarity by utilizing the mean vectors 4t' avoid the interference high uncertain nodes,
o = =a o.
a Similarity .105
(2)

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where je" and pi are the mean representations of i-th and j-th nodes in m-th modality. Similarity a metric function and we adopt Euclidean distance (37] our experiments.
3.2.2 Gaussian Patch-Graph Convolutional Network
After contructing the Gaussian Patch-Graph, we further employ a Gaussian Patch-Graph Convo- lutional Network (GPGCN) to facilitate message passing among distributional nodes. Since each node is modeled as a Gaussian distribution, traditional graph convolution operations are not directly applicable. Thus, we adopt GPGCN to separately conduct message passing based on the mean and variance [38] and thus enable effective propagation of both semantic representations and uncertainty information. Formally, the message passing process is defined as,
(m..+1) 1) = RcLU
(D'M)- & (D") & (m Wa (m,.t
(m,1+1) = ReLU (D" (D'M) (m w0 (m.1)
where ju! (m.00) = 1M and à (m..0) = (m)) 1 0, D corresponding degree matrix of a=". un (m,.1 and wa (m.1) are trainable weight matrices. After stacking multiple layers, we can obtain the final output jt (m,))) and & (m.L), For convenience, let fi" fz (,)))) and 8th = o (m.L)) in the following text. Considering that node representations follow Gaussian distributions, we adopt the sampling operation to obtain final output, i.e., 4t N N( )2 However, this sampling process is not differentiable. Therefore, we employ the reparameterization trick to generate node representations as follows,
1 denotes the I-th layers of GGCN and
tt fa"h + E N~,,
where follows normal distribution. -h = , , ". ...22) represents the final node representa- tions. To alleviate the effect extremely unbalanced standard deviation, we adopt a sigmoid function to map & to [0, 1]. In addition, we also introduce learnable hyperparameter & that can dynamically adjust the constraint strength of the standard deviation according to the data. This allows the model to flexibly cope with different noise levels while maintaining robustness. Finally, to extract more effective embedding of global information, local cues are aggregated into global messages to enhance features as follows,
a.mh [ 7 -
(5)
where 7 denotes a pooling operation. [ -] is the concatenation operation and mm is the transformation matrix. To further promote joint aggregation representation, Kullback-Leibler divergence 33 is applied to the class token as,
heh KLW In (o"))) || (cdo, n) = (1 log(o.)) (1x0) (0
(6)
3.3 Uncertainty-Guiiee Mixture of Experts
Based on the proposed GPGR, we obtain a more abundant feature representation in each modality. To further suppress the possible noise interference in each modality and enhance the multi-modal semantic consistency, we propose the Uncertainty-Gunt -Giided Mixture of Experts (UGMoE) strategy, which aims to achieve robust and deep multi-modal collaboration by modeling the sample-level uncertainty and guiding the sharing of more expressive experts among modalities.
3.3.1 Uncertainty-Guidae Experts Network
In the traditional MoF, the selection of experts is often based on the input content. In the application of multi-modal data, experts are usually selected independently in each modality, which makes easy to ignore the sample noise and complementarity between modalities [36] 40) The proposed experts network can bridge this weakness. To be specific, we first model the uncertainty of the extracted features of each modality to enhance the ability of experts to process samples with different noise levels. The multivariate Gaussian distribution that maps the features of each modality is defined as,
p(2 ( - Nj (d)
where the means ji and variances ē are obtained by two independent fully connected layers, respec- tively. Then, to model the uncertainty, we resample from the distribution as follows,
i + "h e~ (0,
(8)
5

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However, to ensure the stability and robustness of the model expression, we do not directly use the noisy sampled feature 2 the prediction process of the final task, but use the mean as the final feature representation of the sample for the downstream decision modeling. In addition to this, to further the uncertainty modeling capabilities, we introduce the KI. divergence [39 regularization term to ensure that the sample distribution is close to the normal distribution as follows,
C LLLN(= I7", (6"*)² [I [)] (1 + log(o"")2 (a²*)²² (o
3.3.2 Uncertainty-Guiie Routing
To better manage input features effectively, we design an unccrtainty-guiiee routing that includes gate mechanism to gain modal interaction by selecting different modal experts. The routing process first applies a linear transformation the imput feature. Then, using a softmax activation acts on the result of this transformation to obtain probability score S(E") E RC where C' denotes the number of experts. Finally, the TOP: operation is employed select the top k (k = 1) excellent experts of the current modality to learn other modalities, optimizing multi-modal interactions. Thus, each gate involves the total number C' + .M experts, where M denotes the total number of data. Besides, to improve the experts' ability, we further add this constraint term 31 as follows,
M-1
Σ
(āth) STh (Z"))
(10)
C+
As . increases, the corresponding expert assigns smaller weights by this constraint.
3.3.3 Interactive Aggregation
We use gate scores weights fuse the expert output results by the uncertainty-guided routing operation above as follows,
C+M-1
2T
(11)
The learned feature tends to be obtained by specific experts, which means that the existence some experts not be optimized. To solve this problem, further add regular terms [3] as,
CT C+M Σ
1
C+M -1
1
Σ X
1 aargg arg max STH (X =c c)
S (Xm)
B
X
(12)
where B denotes the batch size and X is the features collection of samples in batch for the m-th modality. The former item refers to the proportion of samples assigned to expert c, and the latter item refers to the proportion of weights assigned by the router to expert c. Then, we aggregate interactive features via the concatenation operation as å = [ 2 27
3.4
Train Loss
In the training of the proposed UGG-ReID, we combine multiple loss functions to optimize the overall framework. First, L is used to constrain the global features, which represents the sum of f and LT Then, to improve the network of uncertainty-guidd experts, we introduce 4T which prioritizes experts with low uncertainty by dynamically adjusting expert selection, while adopting C to prevent over-reliance on certain experts. Finally, cross-entropy loss Zee and triplet loss Ciri used to supervise the entire network. This optimization loss can be expressed by the following formula,
L= = + Lirs
Σ
(n + X2 Az
(13)
mE(R.N.))
where \1, X2 and A the balancing coefficients of this overall loss term.
4 Experiments
In this section, we evaluate the effectiveness of the proposed UGG-RelD on five commonly used datasets and compare it with some state-of-the-an methods.

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Table 1: Comparison with state-of-the-art methods the multi-modal person RelD datasets(in %).
RGBNT201
MarKet1501MMM
Methods
Publication
Structure
mAP
R-1
R-5
R-10
mAP
R-1
R-5
R-10
HAMNet
AAAI20
CNN
27.7
26.3
41.5
51.7
60.0
82.8
92.5
95.0
PFNet
AAA121
CNN
38.5
38.9
52.0
58.4
60.9
83.6
92.8
95.5
IEEB
41
AAA122
CNN
46.4
47.1
58.5
64,2
64.3
83.9
93.0
95.7
TIENet
29
TNNLS25
CNN
54.5
54.4
66.3
71.1
67.4
86.1
94.1
96.0
Multi-modal
UniCat
42
NIPSW23
VIT
57.0
55.7
EDITOR
22
CVPR24
VIT
66.7
68.7
82.2
87.9
77.4
90.8
96.8
98.3
RSCNer
43
TCSVT24
VIT
68.2
72.5
HTT 18
AAA124
VIT
71.1
73.4
83.1
87.3
67.2
81.5
95.8
97.8
TOP-ReID
21
AAA124
VIT
72.2
75.2
84,9
89.4
82.0
92.4
97.6
98.6
ICPL.ReID
28
TMM25
CLIP
75.1
77.4
84.2
87.9
PromptMA
24
TIP25
CLIP
78.4
80.9
87.0
88.9
83.6
93.3
MambuPro
19
AAAI25
CLIP
78.9
83.4
89.8
91.9
84.1
92.8
97.7
98.7
DeMo
36
AAAI25
CLIP
79.7
81.8
89.4
92.5
83.6
93.1
97.5
98.7
IDEA 23
CVPR25
CLIP
80.2
82.1
90.0
93.3
UGG-ReID
Ours
CLIP
81.2
86.8
92.0
94.7
94.3
98.4
99.1
4.1
Experiments Setting
Datasets. We conduct five multi-modal object RelD datasets, including two person ReID datasets (e.g.. RGBNT201 19). Marke:1501-MM (41)) and three vehicle datasets (e.g.. MSVR310 (10),
RGBNTI00 (8), WMVEID863 (IT.. These datasets pose multi-dimensional challenges such as
perspective changes and environmental disturbances, reflecting the broad applicability of this method.
Implementation Details. All experiments are conducted using PyTorch single NVIDIA RTX 4090 GPU. A pre-trained CLIP model serves as the visual encoder. Person and vehicle images resized to 256x and 128x256, respectively. We extract features using 16x16 patching strategy, yielding 128 local tokens and one global token, which jointly serve as nodes in a Gaussian patch-graph for modeling. The model is fine-tuned with Adam (learning rate: 0.00035) for 40 epochs. More details of the experiments are provided in the supplementary material.
Evaluation Protocols. We utilize mAP and Rank to evaluate the performance of the model, where mAP means the accuracy of ReID, while Rank shows the probability that the correct match is included in the top results. The combination of the two can more accurately reflect the ability to identify.
4.2 Comparison with State-of-the-Art Methods
We evaluate our proposed UGG-ReID on two multi- modal person ReID datasets in Table We observe that both datasets achieve state-of-the-ate art results for all metrics. Particularly, RGBNT201 [9] outperforms the next most popular method in the metric rank-1 by 3.4%. In addition, method surpasses DeMo (36) in multiple metrics, highlighting the effectiveness of our architectural design. DeMo [36] relies on modality decoupling to preserve specific cues and uses attention assign expert weights in MoE. In contrast, our proposed GPGR builds stronger modality-specific representations by incorporating alcatoric uncertainty from local details. Meanwhile, the UGMoE strategy utilizes sample uncertainty to guide expert selection and applies a novel routing strategy to facilitate more effective multi-modal collaboration.
Evaluation on Multi-modal Person ReID.
Evaluation on Multi-modal Vehicle ReID. We further conduct experiments three multi-modal vehicle datasets, which contain challenges such as large view discrepancies and intense glare con- ditions, fully validate the robustness and effectiveness of the proposed UGG-ReID. As shown in Table [] our method maintains stable performance under various complex interference conditions, clearly demonstrating the practical effectiveness of the proposed method in enhancing the model's generalization capability. Notably, on WMVEID863 [[]] with severe dazzle interference, our method outperforms the suboptimal method by 2.8% and 3.6% in the mAP and R-1, respectively. This result further validates the robustness and effectiveness in the face of significant noise interference.
4.3 Ablation Study
To analyze the contribution each module, we conduct systematic ablation experiments around the two core components, UGMoE and GPGR, RGBNT201 [9] and WMVEID863 [[]] We first
7

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Table 2: Comparison with state-of-thh-arr methods on the multi-modal vehicle ReID datasets(in %)
MSVR310
RGBNT100
WMVEID863
Methods
Publication
Structure
mAP
R-1
R-5
R-10
mAP
R-1
R-5
R-10
HAMNet
AAAI20
CNN
27.1
42.3
74.5
93.3
45.6
48.5
63.1
68.8
PFNet 9
AAAI21
CNN
23.5
37.4
68.1
94.1
50.1
55.9
68.7
75.1
IEEE 41
AAAI22
CNN
21.0
41.0
61.3
87.8
45.9
48.6
64.3
67.9
CCNet
10
INFFUS23
CNN
36.4
55.2
77.2
96.3
50.3
52.7
69.6
75.1
Multi-nodal
EDITOR
22
CVPR24
VIT
39.0
49.3
82.1
96.4
65.6
73.8
80.0
82.3
RSCNet
43
TCSVT24
VIT
39.5
49.6
82.3
96.6
TOP-ReID
21
AAAI24
VIT
35.9
44.6
81.2
96.4
67.7
75.3
80.8
83.5
FACENeT
INFFUS25
VIT
36.2
54.1
81.5
96.9
69.8
77.0
81.0
84.2
PrompiMA
24
TIP25
CLIP
55.2
64.5
85.3
97.4
MambaPro
19
AAAI25
CLIP
47.0
56.5
83.9
94.7
69.5
76.9
80.6
83.8
DeMo 36
AAAI25
CLIP
49.2
59.8
86.2
97.6
68.8
77.2
81.5
83.8
IDEA 23
CVPR25
CLIP
47.0
62.4
87.2
96.5
ICPL-ReID 28
TMM25
CLIP
56.9
77.7
87.0
98.6
67.2
74.0
81.3
85.6
UGG-ReID
Ours
CLIP
60.1
78.0
88.0
98.1
72.6
80.8
84.2
87.2
Table 3: Ablation study results on the RGBNT201 and WMVEID863 datasets (in %).
UGMoE
GPGR
RGBNT201
WMVE.18633
MoE Uneer.
PGR
Uncer.
mAP
R-1
R-5
R-10
mAP
R-1
R-5
R-10
72.2
72.7
82.2
87.3
66.4
72.4
79.4
82.9
(b)
x
X
X
75.0
76.3
89.5
68.7
75.8
80.2
82.9
(c)
V
X
X
76.0
80.4
88.8
91.7
69.7
77.0
81.5
84.5
(d)
V
V
X
77.3
81.7
90.3
92.1
70.2
77.4
83.5
86.7
(e)
V
V
81.2
86.8
92.0
94.7
72.6
80.8
84.2
87.2
use the pre-trained shared CLIP visual encoder as the bascline in Table 3 and gradually introduce each component for comparative analysis. UGMoE. Introducing the traditional Mixture of Experts (MoE) strategy to the baseline can bring some performance improvement in (b). We further incorporate uncertainty modeling into the MoE strategy and the model performance continues to improve. Compared with the baseline, the proposed method improves mAP/R-1 by 4.8%/7.7% and 3.3%/4.6% on RGBNT201 (Σ] and WMVEID863 (II. respectively, which indicates that the introduction of uncertainty helps to estimate the expert's credibility more accurately, thus realizing more reasonable sample assignment and improving the effectiveness of mulli-moda. information fusion. GPGR. In the case of relying only on the above strategy for multi-modie modeling, the modal-specific information is still not fully explored. To further promote the model's 's ability, we introduce GPGR to enhance the modeling ability of fine-grained local cues by using uncertainty. In the experiment, we first evaluate the effect of the standard Patch-Graph Representation (PGR) module in (3 and then integrate the uncertainty into the network. The results show that GPGR delivers significant improvements based on the integrated UGMoE. Compared to using UGMoE alone, our method improves mAP/R-1 by 5.2% on RGBNT201 9 and by 2.9%//.88 on WMVEIDS63 [I]. respectively. This fully shows that GPGR can effectively suppress noise interference and mine richer information. In summary, our proposed method enhances features within each modality and jointly captures complementary information among multi-modal data, improving model robustness and effectiveness. More ablation provided in the supplementary material.
LOIFOR TORSAD DILE-RI
EEETTOR FOR
Beble BUGG-BIINONE
30
70
61
6.
50
48
30 38
Tnlcendy
5-1 Iatenii Bo
Figure 3: Robustness analysis on RGBNT201. Eyaluation results with (a) different levels of Gaussian noise added during dataset generation, and (b) varying noise intensities added during testing after training on clean data.

---

Impert Image
19
DeMsAAARE
TGG-ReID
Figure 4: Retrieval results under different testing conditions after training on clean data. (a) Clean. (b) Gaussian noise. (c) Arbitrary noise. Green/Red boxes indicate correct/incorreet retrieval results.
Table 4: Expert-balanced analysis of Table 5: SOTA methods RGBNT201.
Efficiency analysis and accuracy comparison with
each modality on WMVEID863.
M Score E,
Ex
Es
E4
RGBNT201
Params(M)
FLOPs(G)
FPS
mAP
R-1
Uncer.
0.31
0.27
0.30
0.12
TOP-ReID 21
324.5
35.5
398.9
72.2
75.2
R
Gate
0.07
0.12
0.07
0.74
EDITOR 22
119.3
40.8
335.1
66.7
68.7
PromptMA 24
107.9
36.2
343.5
78.4
80.9
N
Uncer.
0.24
0.24
0,27
MambaPro 19
74.8
52.4
243.2
78.9
83.4
Gate
0.28
0.26
0.18
0.28
DeMo 36
98.8
403.6
79.7
81.8
Uncer.
0.28
0.26
0.29
0.18
IDEA
91.7
299.5
80.2
82.1
T
0.17
0.21
0.14
UGG-ReID
103.2
35.0
371.4
81.2
86.8
4.4
Robustness Analysis
To systematically evaluate the robustness of the proposed UGG-ReID under noise interference, we inject Gaussian noise of different intensities into the RGBNT201 dataset (9) and increase the noise intensity from 5 to 30 to generate multiple noisy versions of the dataset. Compared to the four mainstream methods (EDITÓR (22), TOP-ReID 21, ICPL-ReID (28] and DeMo (36)). the proposed
method maintains superior performance under all noise intensities, as shown in Fig. 3 (a). These
results indicate that the method has good robustness and generalization ability.
Furthermore, we evaluate the model's performance by injecting Gaussian noise of different intensities the testing phase after completing training on clean data. As Fig. (b) shows, the proposed method is stable and superior to other methods under multi-level noise conditions. Meanwhile, we perform rank-list retrieval evaluations on clean data and different types of noise in Fig. The results show that the proposed method achieves excellent performance under various interference conditions, which significantly better than the advanced method DeMo 33). which fully reflects its robustness and practicability under complex perceptual interference.
4.5 Expert Balanced Analysis
We show the router average gate scores and uncertainty scores of all test samples for each modality on WMVEID863 in Table 4 We observe that N and T modalities show a more balanced expert activation distribution, while Expert 4 in the R modality has a heavy weight when the uncertainty low. These results indicate that the routing mechanism dynamically adjusts expert allocation according to different modalities and sample uncertainty.
4.6 Efficiency Analysis
To further validate the effectiveness of our proposed UGG-ReID, we conduct an efficiency analysis, as shown in Table We evaluate the inference speed of each method on the RGBNT201 dataset, using Frames Per Second (FPS) as the evaluation metric. As shown in the Table UGG-ReID reaches an inference speed of 371.4 FPS while maintaining a relatively low parameter count of 103.2 million and computational cost of 35.0G FLOPs. This speed is only slightly lower than that of the lighter DeMo (36), and is significantly higher than most mainstream approaches, including MambaPro [19) and EDITOR (22. Notably, despite its high efficiency, UGG-ReID still achieves excellent results.

---

(0)
(b)
(4)
Figure 5: Visualization results of the (a) Input image. (b) Bascline. (c) UGG-ReID (Ours).
(4) Basdins
(b) Buseline UGME
(c) Basciie UGMoE PGR
(d) LGG-RRRR
Figure 6: T-SNE visualization of extracted features with different model component combinations on the RGBNT201 dataset.
4.7 Visual Results
Multi-modal Activation Maps:
We adopt the Grad-CAM method 42 visualize the extracted features of each modality. Samples with backpacks are above the dotted line, and samples with- backpacks are below the dotted line. The proposed UGG-ReID consistently attends more semantically rich object regions compared to the baseline model that lacks our module, as shown in Fig. [ Notably, under challenging conditions such as motion blur occlusion, our method still effectively focuses on key target features, further validating its robustness and discriminative power. This suggests that our method effectively captures the diversity of multi-modas information through uncertainty-guie guided multi- local and sample- joint learning, which significantly improves the robustness and the performance of object re- re-identification.
Multi-modal Feature Distributions:
We adopt the T-SNE method 45 to visualize the extracted features intuitively show the feature distribution of the model under different combinations of mod- ules. With the gradual introduction of the proposed modules, the feature distribution gradually shows obvious clustering in Fig. 66] This compact clustering structure consistent with the performance enhancement results in Table 3 which further validates the effectiveness of our method in enhancing modal feature representation and multi-modal interaction modeling. More visualizations are provided in the supplementary material.
Conclusion
In this paper, we propose a novel Eicertainty-Guidee Graph model for multi- modal object (UGG- ReID), effectively mining modal-specific information and boosting modal collaboration for multi- modal object ReID. This UGG-ReID consists of two main aspects. One design is the Gaussian Patch-Graph Representation (GPGR) model for quantifying the aleatoric uncertainty of local and global features and modeling the relationship between them. This manner mitigates the noise interference and enhances the learning ability and robustness of modality-specific information. The other introduces the Uncertainty- Guidedd Mixture of Experts (UGMoE) strategy to select appropriate experts based on sample uncertainty and facilitate deep multi-modal interactions through novel routing mechanism. These two core schemes synergistically optimize the overall network structure and achieve the current optimal performance all five multi-modal object ReID datascts.
10

---

Acknowledgements
This work was supported by the National Natural Science Foundation of China (62372003, 62576004), the Natural Science Foundation of Anhui Province (2308085Y40, 24080851037), the Key Technolo- gies R&D Program of Anhui Province (202423k09020033), and the Open Research Project of the Anhui Provincial Key Laboratory of Security Artificial Intelligence (SA1202401).
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A Supplementary Material
In the supplementary materials, we provide a detailed description of the UGMoE strategy and present additional experiments to further validate the robustness and effectiveness of the proposed UGG-ReID.
A.1 Details of UGMoE
In the main text, we set the value in Top k to be 1. In this case, each modality will have C - unique experts and M shared experts, the total number of experts is C M If k: * analyze it further to get C + k(M 1) experts for each modality. Thus, Eq. 10 is defined as follows,
C+K(MS-1)
C C k(M 1)
Σ
(0Th) St (
(14)
Next, we utilize gate scores as weights to fuse the expert output results by the above routing operation as follows,
C++(M-1)
2Th
Σ
So(i"))
Ec("")),("")) (
(15)
The learned feature tends be obtained by specific experts, which means that the existence of some experts can not be optimized. To solve this problem, we further add regular terms [31] as,
C+
( B
CT = C+KKMM -1
Σ
Σ X
if
arg max STh (P(M) = c)
Σ X
Sth Sth( (8m)
B
(16)
where denotes the batch size and X is the features collection of samples in batch for the m-th modality. The former item refers the proportion of samples assigned expert c, and the latter item refers to the proportion of weights assigned by the router to Expert c. B denotes the batch size. Finally, we aggregate interactive features via the concatenation operation [ aN #1..
A.2 Comparison with Prior Works
For conceptual comparison, we first utilize a (laussian-base random graph for object representation, where nodes described by Gaussian distributions to represent the uncertainty of image patches in the presence of noise, whereas previous works generally employ a deterministic graph model, represented by feature vector. We design Gaussian Patch- Patch-Graph Representation (GPGR) to quantify aleatoric uncertainties for global and local features while modeling their relationships. To our knowledge, this work is the first attempt exploit a random patch-graph model for the object ReID problem. Second, for the Mixture-of-Expeer Mixture of Experts (UGMoE) strategy, which enables different samples select experts based on uncertainty and utilizes an uncertainty guuiddd routing mechanism to strengthen the interaction between multi-modes features, effectively promoting modal collaboration.
approach, we design the Uncertainty- Guidedd
For a technical comparison, we first further analyze the impact of different uncertainty modeling approaches the performance of multi-modal object ReID [1] 30 31. As shown in Table works EUAR 31 and EAU [ are able to perceive inter-sample uncertainty. In contrast, MAP [30 introduces a more comprehensive uncertainty modeling mechanism to quantify the uncertainty
Table 6: Component-win wise comparison of different methods on RGBNT201 (in %).
Method
Uncer.
MoE
Local
Gloabl
Graph
mAP
R-1
EUAR
x
74.1
77.6
EAU
V
x
75,6
80.3
MAP
30
X
V
V
x
76.8
78.2
DeMo
36
V
V
V
x
79.7
81.8
UGG-ReID
V
V
V
81.2
86.8
21

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of local cues. Although the above methods take uncertainty into account, they either neglect the modeling of uncertainty in local cues or the structural relationships between local regions. Second, we perform comparisons under different MoE strategies. As shown in T we substitute two existing MoE methods [31 36]. Compared with DeMo [36,, UGMoE better exploits the diversity of samples by introducing uncertainty modeling, and compared with EUAR [1.. UGMoE further strengthens the interaction between different modalities.
A.3 Details of Experiments
A.3.1 Experiments Setting
Datasets. To comprehensively evaluate the generalization ability the proposed UGG-ReID frame- work, conduct experiments five public datascts. These include person re-identification datasets, RGBNT201 [9] and Market1501-MM
[41]. as well as three challenging vehicle re-
identification benchmarks: MSVR310 [0]. RGBNT100 [8) and WMVEID863 [11]. These datasets collectively reflect a wide range of real-world scenarios and associated challenges. Table summa- rizes the partition protocols and the specific challenges posed by each dataset.
Table Details of the datasets partition settings and their corresponding challenges, */* represents ID/Sample.
RGBNT201
Marke11501-MM
MSVR310
RGENTI00
WMVEID863
Train Query
171/3951
751/12936
155/1032
50/8675 50/1715
603/10446
3MM836
750/3368
52/591
210/290
Gallery
30836
751/15913
155/1055
50/8575
272/3678
Wide Views, Occlusions
Simulate the Night Scene
Longer Time Span, Complex Conditions
Different Views,
Challenges
Intense Flare
Illumination Issue
Implementation Details. For all experiments, we set the number of experts at = 4 and utilize k for the TOP, selection. The loss terms are weighted with A 0.1 and )2 A 0.0001, respectively. The number of layers for GPGCN L. is set to 2. Our code is implemented in Python using the PyTorch framework and will be released publicly upon acceptance.
A.3.2 Ablation Analysis
To verify the role each loss in the model, we conduct systematic ablation experiments, as shown in Table Le., represents the sum of C. and L., which is used to impose constraints on the global token. From The experimental results, one can observe that when the Le., constraint the global token is removed, the performance of the model on multiple evaluation indicators decreases. indicating that the constraint has positive effect on improving modeling ability. Then, CT" is removed to verify performance for adding the loss constraint on the expert. We can find that adding the loss, our mAP/R-1 increases by 2.2% 1 1% and 2.0% 7..% in RGBNT201 respectively, which verifies its enhancement effect on the expert selection strategy Finally, we verify the effectiveness of L which aims to ensure that the number of similar samples assigned to each expert in the training process is balanced. Meanwhile, the expert weights relatively evenly distributed among the experts, and the experimental results show that can effectively prevent the imbalance of distribution among experts and improve the ability of the model.
and WMVEID863 [[],,
Table 8: Ablation results for different loss on the RGBNT201 and WMVEID863 datasets (in %).
Loss
RGBNT201
WMVEID863
Type
mAP
R-1
R-5
R-10
mAP
R-1
R-5
R-10
(a) w/o La.
78.8
84.8
89.6
91.7
69.6
76.9
82.4
85.8
(b)
w/o L,
79.0
83.7
91.6
94.1
70.6
79.2
84.2
86.8
(c)
w/o L.
81.0
84.6
90.7
92.5
71.2
78.3
84.5
87.7
(d)
UGG-ReID
81.2
86.8
92.0
94.7
72.6
80.8
84.2
87.2
22

---

Table 9: Results of the analysis on hyperparameters C' and & on the RGBNT201 and WMVEID863
datasets (in %).
RGBNT201
WMVEID863
RGBNT201
WMVEID863
Expert C'
Top k
mAP
R-1
mAP
R-1
mAP
R-1
mAP
R-1
80.3
83.7
71.4
78.8
78.3
83.1
71.3
78.8
80.8
85.2
72.0
80.1
1
81.2
86.8
72.6
80.8
81.2
86.8
72.6
80.8
79.9
84.6
71.7
79.2
80.0
84.6
72.0
79.7
80.2
85.3
71.2
78.5
80.1
83.7
71.1
78.1
79.0
81.6
70.3
77.6
Table 10: Results of the analysis hyperparameters and on the RGBNT201 dataset (in %)..
RGBNT201
RGBNT201
Layers L
Nodes n
mAP
R-1
mAP
R-1
mAP
R-1
mAP
R-1
80.1
85.3
91.3
93.9
32
79.3
83.7
91.3
93.9
2
81.2
86.8
92.0
94.7
64
80.1
82.9
90.0
94.0
79.1
84.6
90.8
93.3
81.4
84.6
90.4
92.6
4
78.4
82.5
90.3
92.8
128
81.2
86.8
92.0
94.7
5
76.3
80.7
89.1
91.7
160
79.8
83.6
90.2
91.9
A.3.3 Hyperparameter Analysis
We analyze the effects of the hyperparameters C' and on model performance, where C' controls the number of experts and & denotes the number shared experts selected for each modality. As shown in Table 9 moderate increase in C enhances the model's expressive capacity, while appropriate choice of i: strikes balance between stability and flexibility. This facilitates dynamic collaboration and complementarity among experts, ultimately improving overall model performance.
We further analyze the local nodes of GPGL and Layers L GPGCN of the GPGR for the effect of the model in the RGBNT201 dataset in Table I0 For nodes we observe that n=128 achieves excellent results. Too few nodes are not enough to cover rich local information, and too many introduce redundancy and noise. interfering with graph structure learning. For layers L, GPGCN works best when L=2. The number of layers is too shallow and may lead to insufficient fusion of local structures, while too deep may cause over-smoothing, resulting in the loss of discriminative representation of nodes and weakening the expression ability of local discriminative features.
A.3.4 Visual Results
Visualization of Rank List. To analyze the performance of the proposed UGG-RelD method in cross-camera retrieval scenarios, we perform rank-list visualization of the retrieval results of different methods as Fig. 7 Compared with baseline and baseline+ UMoE, UGG-ReID can rank the objects more accurately, demonstrating stronger model robustness and discriminative ability.
Visualization of Class Activation Maps. As shown in Fig, we visualize the proposed UGG-ReID using Class Activation Maps (CAMs) 44 The results further demonstrate that approach is capable capturing discriminative local regions, even under complex environmental conditions.
A.4
Discussion
Multi-modal object RelD exploits fine fine-graing ane-gnained local cues and the complementary information of modalities effectively enhance the robustness and accuracy recognition in complex scenarios [9]
21
As is well known, significant distributional differences exist among different modalities, and noise arising from sample quality and environmental factors further impacts accuracy of feature representations. The proposed UGG-RelD effectively guides the feature fusion process by explicitly quantifying local and sample-level epistemic uncertainties and modeling the relationship between them, enhancing the model's 's robustness and effectiveness. UGG-ReID is the
23
24 36].
23

---

R-1
R-10
RID
(1)
(0)
(n))
2
(b)
(00
#ID 3
(b)
(0)
[m]
(b)
(0)
Figure 7: Rank-list visualizations for four persons from the RGBNT201 dataset under different model configurations: (a) Baseline, (b) Baseline + UGMoE, and (c) UGG-ReID (Ours).
(a)
(b)
(6)
Figure 8: Visualization of Class Activation Maps (CAMs) under different environmental conditions
for six vehicles from the WMVEIDS63 dataset: (a) Normal, (b) Occlusion, and (c) Intense Flare.
first work that leverages uncertainty to quantify fine-grained-local details and explicitly model their dependencies in multi-modal data.
Limitations and In the Future. Our framework employs uncertainty-guided learning enhance robustness against local noise; it may still struggle under extreme conditions where local cues are heavily corrupted or missing.
future work, we will focus on advancing uncertainty quantification and reasoning techniques, exploring the integration of Bayesian inference and evidence theory into multi-modal object ReID [46 48). This aims to enhance the model's robustness to modality and label noise, thereby improving its overall performance and reliability in complex environments.
24


Reviews:
[{"review_id": "jaWqz8hKZ3", "reviewer": "Reviewer_6275", "summary": "The 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.", "questions": "1. 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.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "The 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.", "was_revised": true, "final_justification": "I think the author's response has solved my main concerns. Meanwhile, this result and discussion should be updated in the final manuscript.", "rebuttal": "# Response to Reviewer 6275\nWe thank the reviewer for your useful comments and suggestions.  \n**Q1**. 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\n**A1**.  (1) Here, the local noise refers to the disturbance of the object's local area that commonly exists in multi-modal ReID, as shown in Fig. 1 (a). For example, in thermal infrared images, intense thermal radiation may lead to overexposure of local areas (such as lights and the front of the vehicle), resulting in the loss of structural information. In visible light images, uneven lighting, background occlusion, or equipment sunshades can blur or obscure critical areas (e.g., windows, license plates). These local noises can affect the semantic patch-level learning between different modalities, which influences the final retrieval performance. \n(2) To mitigate the above problem, we design the GPGR module to quantify local uncertainty and extract stable structural relationships, resulting in robust fine-grained characterization, as shown in Fig. 1 (b). In addition, we propose a UGMoE strategy to guide modal interaction and compute the overall uncertainty of the sample to dynamically select experts and also utilize an uncertainty-guided routing mechanism to strengthen the interaction between multi-modal data, as shown in Fig. 1 (c). \n\n\n**Q2**. (1) Using graph nodes is not fresh in the ReID. HOReID [1] is a typical strategy that uses the graph network to solve the missing information. What is the advantage of this work? (2) Meanwhile, I think it will be more specific and interesting if using the graph passing to achieve interaction between different modalities.\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\n**A2**. (1) Comparing with existing graph-based ReID methods, the key difference is that we exploit Gaussion based **random** graph for object representation, whose nodes are described via Gaussian distributions to represent the uncertainty of image patches with noise influence, while previous works generally use a deterministic graph model whose nodes are represented via a feature vector.\nThe main advantages of our method are as follows: we design a Gaussian Patch-Graph Representation (GPGR) to **quantify aleatoric uncertainties for global and local features while modeling their relationships**. GPGR can further alleviate the impact of noisy data and effectively reinforce modal-specific information. To our knowledge, this work is the first attempt to exploit a **random patch-graph model** for the object ReID problem.\n(2) We appreciate your insightful suggestion. We think leveraging graph message passing to model interactions across different modalities is an interesting direction. In future work, we plan further to explore multi-modality integration under our random graph-based framework to improve multi-modal interaction.\n\n**Q3**. 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\n**A3**. The UGMoE module in Fig. 2 only shows the UGMoE work process for the T modality as an illustrative example. In practice, we treat all modalities equally in the proposed UGMoE framework. Each modality has its own specific experts and top-$k$ shared experts of other modalities, which are guided by the corresponding uncertainty estimation. For example, Fig. 2 (b) only shows the T modality and top-$k$ shared experts of R and N modalities.\n\n**Q4**. 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**A4**. Yes, we have visualized some Class Activation Map (CAM) of our method in the WMVEID863 dataset in the Supplementary Material, as shown in Fig. 7. It shows that our method can focus on key areas even when local features are strongly disturbed. During rebuttal, we conduct some more visualizations to compare the main modules, which show significant improvements in feature selection and regional focus. Our visualization experiments show promising results that demonstrate the effectiveness of each module. We will include them in our final version. \n\n#### Table. Results of all test samples' average uncertainty and expert gate score for each modality. \n|||||||\n|:-:|:-:|:-:|:-:|:-:|:-:|\n| Modality | Score | Expert 1 | Expert 2 | Expert 3 | Expert 4 |\n| R           | Unc.  | 0.31     | 0.27     | 0.30     | 0.12     |\n|              | Gate  | 0.07     | 0.12     | 0.07     | 0.74     |\n| N          | Unc.  | 0.24     | 0.24     | 0.27     | 0.24     |\n|             | Gate  | 0.28     | 0.26     | 0.18     | 0.28     |\n| T          | Unc.  | 0.28     | 0.26     | 0.29     | 0.18     |\n|             | Gate  | 0.17     | 0.21     | 0.14     | 0.48     |\n|||||||\n\nIn addition, we quantitatively analyze all test samples' average uncertainty and expert gate score for each modality in the above Table. The results show that there are significant differences in uncertainty and expert allocation between different modalities. For example, in the R modality, expert four is assigned a higher weight due to its lower uncertainty, while other experts receive lower weights; In contrast, the uncertainty and routing weight distribution in N modality are more balanced, indicating that the model can adaptively assign experts according to the characteristics of different modalities, achieving more robust collaborative learning."}, {"review_id": "wZiEceTBnO", "reviewer": "Reviewer_4UnX", "summary": "This 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.", "questions": "Please 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.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Pros:\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", "was_revised": true, "final_justification": "Considering the authors have solved my major concerns, I decide to raise my score to 4 in final decision.", "rebuttal": "# Response to Reviewer 4UnX\nWe thank the reviewer for your useful comments and suggestions.  \n**Q1**. The paper mainly focuses on dealing with local noise, which has been researched by several previous works; strategies like patch-graph and uncertainty estimation have been widely applied before. \nBesides, a branch of related works that focuses on multimodal noise [1-3] in ReID has not been discussed or compared by the authors.\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\n**A1**. Yes, patch-graph and uncertainty estimation have been studied to deal with local noises in previous works [1] [26] [32]. However, the key difference is that we propose to deal with local patch nodes by leveraging a novel **Gaussion based random graph** learning model, which provides a unified model to integrate the advantages of both the patch-graph model and uncertainty estimation strategy. To our knowledge, this work is the first attempt to exploit a **random patch-graph model** for the object ReID problem.  \n\nIn addition, we compare our method with some other previous multi-modal noise [1-3] in ReID as follows. First, previous works [1] [2] focus on label noises in cross-modal alignment. In contrast, we focus on visual (patch) noises in multi-modal fusion.\nSecond, work [3] focuses on label noise at the annotation level, emphasizing data filtering and sample hierarchical training. Differently, we focus on the robustness of the patch modeling phase, especially in terms of sensing input perturbations (e.g., occlusion, blurring noise). Works [1-3] are more suitable for labeling unclean datasets, while UGG-ReID is better at uncertainty modeling of object representation in real complex scenarios. \nIn the current version, we mainly focus on multi-modal ReID correlation methods and multi-modal methods related to uncertainty modeling. In our final version, we will include the above works [1-3].\n\n**Q2**. I appreciate that the authors try to deal with noise through MoE strategies. However, I am concerned 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 a visualization of the router to show whether the experts are balanced.\n\n**A2**. Empirically, our method can obtain relatively balanced results between different experts. Here, we show the router gate scores and uncertainty average scores of all test samples for each modality on WMVEID863 in Table 1. We observe that N and T modalities show a more balanced expert activation distribution, while Expert 4 in the R modality has a heavy weight when the uncertainty is low. These results indicate that the routing mechanism dynamically adjusts expert allocation according to different modalities and sample uncertainty.\n\n#### Table 1. Results of all test samples' average uncertainty and expert gate score for each modality. \n|||||||\n|:-:|:-:|:-:|:-:|:-:|:-:|\n| Modality | Score | Expert 1 | Expert 2 | Expert 3 | Expert 4 |\n| R           | Unc.  | 0.31     | 0.27     | 0.30     | 0.12     |\n|              | Gate  | 0.07     | 0.12     | 0.07     | 0.74     |\n| N          | Unc.  | 0.24     | 0.24     | 0.27     | 0.24     |\n|             | Gate  | 0.28     | 0.26     | 0.18     | 0.28     |\n| T          | Unc.  | 0.28     | 0.26     | 0.29     | 0.18     |\n|             | Gate  | 0.17     | 0.21     | 0.14     | 0.48     |\n|||||||\n\n**Q3**. Provide wall-clock time costs of UGG-ReID against SOTAs.\n\n**A3**. We evaluate the inference speed of each method on RGBNT201, using Frames Per Second (FPS) as the evaluation metric. As shown in Table 2, UGG-ReID reaches an inference speed of 371.4 FPS while maintaining a relatively low parameter count of 103.2 million and a computational cost of 35.0G FLOPs. This speed is only slightly lower than that of the lighter DeMo, and is significantly higher than most mainstream approaches, including MambaPro and EDITOR. Notably, despite its high efficiency, UGG-ReID still achieves strong retrieval results.\n\n#### Table 2. Wall-clock efficiency and accuracy comparison with SOTA methods on RGBNT201.\n|||||||\n|:-:|:-:|:-:|:-:|:-:|:-:|\n| **Method**        | **Params (M)↓** | **FLOPs (G)↓** | **FPS↑** | **mAP↑** | **R-1↑** |\n| TOP-ReID [20]    | 324.5        | 35.5        | 398.9   | 72.2   | 75.2   |\n| EDITOR [21]       | 119.3        | 40.8        | 335.1   | 66.7   | 68.7   |\n| PromptMA [23]  | 107.9        | 36.2        | 343.5   | 78.4   | 80.9   |\n| MambaPro [18]  | 74.8         | 52.4        | 243.2   | 78.9   | 83.4   |\n| DeMo [31]         | 98.8         | 35.1        | 403.6   | 79.7   | 81.8   |\n| IDEA [22]           | 91.7         | 43.7        | 299.5   | 80.2   | 82.1   |\n| **UGG-ReID (Ours)** | **103.2**    | **35.0**     | **371.4** | **81.2** | **86.8** |\n|||||||"}, {"review_id": "wqfSs3m9hJ", "reviewer": "Reviewer_9cgN", "summary": "This 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.", "questions": "In 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.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "please see the comments.", "rebuttal": "# Response to Reviewer 9cgN\n\nWe thank the reviewer for your useful comments and suggestions. \n\n**Q1**. In 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? \n\n**A1**. We also observe a performance degradation compared to DeMo after adding Gaussian noise. This phenomenon essentially stems from the design mechanism of UGG-ReID: introducing the uncertainty of Gaussian distribution modeling nodes and propagating neighborhood information through the graph structure, capturing perturbations in local features more sensitively. When Gaussian noise is injected, this noise directly acts on the uncertainty distribution of our modeling. This is not a reflection of the lack of robustness, but reflects the model's high perception of input changes, which is in line with our original design intention of wanting it to be adaptive in real and complex scenarios.\nAdditionally, UGG-ReID consistently outperforms DeMo [27] at different noise intensities injected. Under real noise conditions (e.g., severe glare interference in WMVEID863), our method outperforms DeMo [27] by 3.8% mAP and 3.6% R-1 in Table 2 of this paper, confirming its superior robustness and discriminative power in complex conditions.\n\n**Q2**. In Section 3.3.1, a new loss does not appear to be included in the overall training objective. Could you provide an explanation for this omission?\n\n**A2**. We introduce an auxiliary loss $\\mathcal{L}^m_s$ for global features constraints in Section 3.3.1, which is a wording omission. In fact, this loss has been included in the overall training objective. The $\\mathcal{L}^m_c$  in Eq. (13)  should be written as $\\mathcal{L}^m_{c,s}$, which represents the sum of $\\mathcal{L}^m_c$ and $\\mathcal{L}^m_s$.\nIn addition, we perform an ablation experimental analysis of this loss in the Supplementary Material, as Table 5. We apologize for the misrepresentation and will make corrections in the final version.\n\n**Q3**. Could the authors elaborate on the theoretical motivation for using Gaussian distributions to model local uncertainties? \n\n**A3**. Yes, we adopt Gaussian distributions to model local features primarily to capture the inherent uncertainty and variability within each image patch. By representing each local patch as a Gaussian, we are able to encode both its mean and variance representation, which reflects the reliability or confidence of the local information. This designed Gaussian Patch-Graph Representation (GPGR) can **quantify aleatoric uncertainties for local features while modeling their relationships**. To our knowledge, this work is the first attempt to exploit a **random patch-graph model** for the fine-grained local noise problem.\n\n**Q4**. Why is this choice particularly appropriate in the context of multimodal ReID? Have 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? \n\n**A4**. Thank you for your question. The framework is highly versatile in its multi-modal design, and the core reason is the synergy of two key aspects:\n(1) The GPGR module effectively enhances the fine-grained information expression ability of the current mode through a graph modeling strategy, which is an infrastructure that can be directly migrated to other multi-modal tasks.\n(2) The UGMoE module realizes the modal fusion of task awareness by modeling sample uncertainty, and its design has strong adjustability.\nWhile we are currently focusing on the ReID task, the framework's ideas can also be applied to other multi-modal applications, such as cross-modal retrieval. For such tasks, we can further refine UGMoE to enhance semantic alignment and compress inter-modal distances. However, challenges may arise during this expansion, such as increased modality-specific discrepancies and limited stability in uncertainty estimation. We will investigate these issues more thoroughly in future work.\n\n**Q5**. To what extent is the performance of UGG-ReID sensitive to key hyperparameters.\n\n**A5**. We analyze the effects of the hyperparameters $C$ and $k$ on the UGMoE module in the Supplementary Material, as Table 6.\nWe further analyze the local nodes $n$ of GPGL and layers $L$ of GPGCN in the GPGR for the effect of the model on RGBNT201, as below Table 3. For nodes n, we observe that $n$=128 achieves excellent results. Too few nodes are not enough to cover rich local information, and too many introduce redundancy and noise, interfering with graph structure learning. For layers $L$, GPGCN works best when $L$=2. The number of layers is too shallow and may lead to insufficient fusion of local structures, while too deep may cause over-smoothing, weakening the expression ability of local discriminative features. \n\n#### Table 3. Results of the analysis on hyperparameters $n$  and  $L$ of the GPGR on RGBNT201.\n||||||\n|:-:|:-:|:-:|:-:|:-:|\n|Nodes $n$|mAP|R-1|R-5|R-10|\n|32|79.3|83.7|91.3|93.9|\n|64|80.1|82.9|90.0|94.0|\n|96|81.4|84.6|90.4|92.6|\n|128|81.2|86.8|92.0|94.7|\n|160|79.8|83.6|90.2|91.9|\n||||||\n\n||||||\n|:-:|:-:|:-:|:-:|:-:|\n|Layers $L$|mAP|R-1|R-5|R-10|\n|1|80.1|85.3|91.3|93.9|\n|2|81.2|86.8|92.0|94.7|\n|3|79.1|84.6|90.8|93.3|\n|4|78.4|82.5|90.3|92.8|\n|5|76.3|80.7|89.1|91.7|\n||||||\n\n**Q6**. The 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. \n\n**A6**. Thanks for your suggestion. The main aspect of Gaussian graph convolution is to conduct information aggregation with each neighborhood set. Here, we achieve Gaussian graph convolution from both a mean and a variance perspective. Theoretically, the outputs of the Gaussian graph convolutional layer also obey a Gaussian distribution. \nThe main complexity of our Gaussian graph convolution involves two aspects, i.e., mean-based aggregation and variance-based aggregation. Thus, the overall complexity is generally twice as complex when comparing with regular patch-graph convolution. \nWe will include this analysis in our final version. \n\n**Q7**. The 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. \n\n**A7**. First, for Gaussian modeling, we exploit Gaussian based **random** graph for object representation whose nodes are described via Gaussian distributions to represent the uncertainty of image patches with noise influencing, while previous works generally use a deterministic graph model whose nodes are represented via a feature vector. We design a Gaussian Patch-Graph Representation (GPGR) to **quantify aleatoric uncertainties for global and local features while modeling their relationships**. To our knowledge, this work is the first attempt to exploit a **random patch-graph model** for the object ReID problem. \nSecond, for Mixture-of-Experts, we design the Uncertainty-Guided Mixture of Experts (UGMoE) strategy, which makes different samples select experts based on the uncertainty and also utilizes an uncertainty-guided routing mechanism to strengthen the interaction between multi-modal features, effectively promoting modal collaboration.\n\n**Q8**. provide support for how samples are distributed across experts, nor is the actual benefit of such routing quantitatively analyzed.\n\n**A8**. We quantitatively analyze the router gate scores and uncertainty average scores of all test samples for each modality on WMVEID863 in Table 3 below. The results show that the model can adaptively assign experts according to the uncertainty of samples, achieving more robust collaborative learning.\n\n#### Table 3. Results of all test samples' average uncertainty and expert gate score for each modality on WMVEID863.\n|||||||\n|:-:|:-:|:-:|:-:|:-:|:-:|\n|Modality|Score|Expert 1|Expert 2|Expert 3|Expert 4|\n|R|Unc.| 0.31|0.27|0.30|0.12|\n|| Gate|0.07|0.12 |0.07|0.74|\n|N| Unc.|0.24|0.24|0.27|0.24|\n||Gate|0.28|0.26|0.18|0.28|\n|T|Unc.|0.28|0.26|0.29|0.18|\n||Gate|0.17|0.21|0.14|0.48|\n|||||||\n\n**Q9**. Lacks details on model size or inference latency.\n\n**A9**. We evaluate the model size and inference latency against SOTAs on RGBNT201. As Table 4, UGG-ReID reaches an inference speed of 371.4 FPS while obtaining a relatively low parameter count of 103.2M and a computational cost of 35.0G FLOPs. This speed is only slightly lower than that of the lighter DeMo [31], and is higher than most mainstream approaches, including MambaPro and EDITOR. Notably, despite its high efficiency, UGG-ReID still achieves excellent retrieval results.\n\n#### Table 4. Comparison of model efficiency with state-of-the-art methods on RGBNT201.\n|||||||\n|:-:|:-:|:-:|:-:|:-:|:-:|\n|Method|Params (M)↓| FLOPs (G)↓|FPS↑|mAP↑|R-1↑|\n|TOP-ReID [20]|324.5|35.5|398.9|72.2|75.2|\n|EDITOR [21]|119.3 |40.8|335.1|66.7|68.7|\n|PromptMA [23]|107.9|36.2|343.5|78.4|80.9|\n|MambaPro [18]|74.8|52.4|243.2|78.9|83.4|\n|DeMo [31]| 98.8|35.1|403.6|79.7|81.8|\n|IDEA [22]| 91.7|43.7| 299.5|80.2 |82.1|\n|UGG-ReID (Ours)|103.2| 35.0|371.4|81.2|86.8|\n|||||||\n\n**Q10**. The 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\n**A10**. We not only evaluate our method under synthetic noise but also on WMVEID863, which captures real-world multi-modal noise conditions, including glare and occlusion. Our method achieves improvements of 2.8% mAP and 3.6% R-1 over the secondary models in Table 2 of this paper, indicating its effectiveness in handling real-world interference."}, {"review_id": "LQRjuDmw3j", "reviewer": "Reviewer_9LdQ", "summary": "This 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.", "questions": "According 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.", "limitations": "No", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "I keep my score after reading the rebuttal. The authors should further polish the paper to make it acceptable.", "rebuttal": "# Response to Reviewer 9LdQ\n\nWe thank the reviewer for your useful comments and suggestions. \n\n**Q1**. The authors should improve the paper's clarity, including full terms and their abbreviations, repeatedly introduced parts, and terms without explanations.\n\n**A1**. In the final version, we have carefully revised the full text, including unified terminology, supplementing undefined abbreviations, removing duplicate descriptions, and further clarifying key concepts to improve overall readability and academic normability.\n\n**Q2**. 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.\n\n**A2**. For conceptual comparison, first, for Gaussian modeling, we exploit a Gaussian-based **random** graph for object representation, whose nodes are described via Gaussian distributions to represent the uncertainty of image patches with noise influencing, while previous works generally use a deterministic graph model whose nodes are represented via a feature vector. We design a Gaussian Patch-Graph Representation (GPGR) to **quantify aleatoric uncertainties for global and local features while modeling their relationships**. To our knowledge, this work is the first attempt to exploit a **random patch-graph model** for the object ReID problem. \nSecond, for Mixture-of-Experts, we design the Uncertainty-Guided Mixture of Experts (UGMoE) strategy, which makes different samples select experts based on the uncertainty and also utilizes an uncertainty-guided routing mechanism to strengthen the interaction between multi-modal features, effectively promoting modal collaboration.\n\nFor technical comparison, first, we further analyze the impact of different uncertainty modeling approaches on the performance of multi-modal object ReID [1] [26] [32]. As shown in the Table, works EUAR [32], EAU [1] are able to perceive the inter-sample uncertainty. In contrast, MAP [26] introduces a more comprehensive uncertainty modeling mechanism to quantify the uncertainty of local cues. Although the above methods take uncertainty into account, they either neglect the modeling of uncertainty in local cues or the structural relationships between local regions. Second, we perform comparisons under different MoE strategies. As shown in this Table, we substitute two existing MoE methods [27] [32]. Compared with DeMo [27], UGMoE better exploits the diversity of samples by introducing uncertainty modeling, and compared with EUAR [32], UGMoE further strengthens the interaction between different modalities.\n\n#### Table. Component-wise comparison of different methods on RGBNT201.\n|||||||||\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n| Method         | Unc. | MoE | Local | Global | Graph | mAP  | R-1  |\n| EUAR [32]      | √    | √   |       | √      |       | 74.1 | 77.6 |\n| EAU [1]        | √    |     |       | √      |       | 75.6 | 80.3 |\n| MAP [26]       | √    |     | √     | √      |       | 76.8 | 78.2 |\n| DeMo [27]      |      | √   | √     | √      |       | 79.7 | 81.8 |\n| **UGG-ReID (Ours)** | √    | √   | √     | √      | √     | **81.2** | **86.8** |\n|||||||||\n\n**Q3**. The authors are encouraged to supplement the Limitations section.\n\n**A3**. Thanks to the reviewer for your suggestions. We will add a Limitations section to the final manuscript to supplement the current limitations. While our framework employs uncertainty-guided learning to enhance robustness against local noise, it may still struggle under extreme conditions where local cues are heavily corrupted or missing. Additionally, its performance across modalities has not been extensively studied, which limits its applicability in some real-world deployments."}]

Author rebuttal:

Meta-review: This paper addresses the Multi-Modal Object Re-Identification task and, following the rebuttal phase, received one clear acceptance rating along with three borderline-positive scores.

In the rebuttal, this paper addressed most of the novelty concerns, including expert experiments and mathematical, theoretical analysis of multimodality. Overall, AC agrees with the reviewers, believing that this paper still offers significant strengths for fine-grained Re-ID, such as its analysis of uncertainty and graph models for the current task, and its clear performance improvements.

AC noted some shortcomings in the paper and understands its borderline and positive responses. AC requested that the authors incorporate the key experiments mentioned in the rebuttal into the final version to further enhance the paper.
Decision: Accept (poster)