Dual-Branch Representations with Dynamic Gated Fusion and Triple-Granularity Alignment for Deep Multi-View Clustering
Authors: Wenyuan Kong
Zhibin Gu
Bing Li
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-View Clustering
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: Multi-view clustering seeks to exploit complementary information across different views to enhance clustering performance, where both semantic and structural information are crucial. However, existing approaches often bias toward one type of information while treating the other as auxiliary, overlooking that the reliability of these signals may vary across datasets and that semantic and structural cues can provide complementary and parallel guidance. As a result, such methods may face limitations in generalization and suboptimal clustering performance. To address these issues, we propose a novel method, Dual-branch Representations with dynamic gatEd fusion and triple-grAnularity alignMent (DREAM), for deep multi-view clustering. Specifically, DREAM disentangles semantic information via a Variational Autoencoder (VAE) branch, while simultaneously captures structure-aware features through a Graph Convolutional Network (GCN) branch. The resulting representations are dynamically integrated using a gated fusion module that leverages structural cues as complementary guidance, adaptively balancing semantic and structural contributions to produce clustering-oriented latent embeddings. To further improve robustness and discriminability, we introduce a triple-granularity feature alignment mechanism that enforces consistency across views, within individual samples, and intra-cluster, thereby preserving semantic-structural coherence while enhancing inter-cluster separability. Extensive experiments on benchmark datasets demonstrate that DREAM significantly outperforms SOTA approaches, highlighting the effectiveness of disentangled dual-branch encoding, adaptive gated fusion, and triple-granularity feature alignment.

Published as a conference paper at ICLR 2026
DUAL-BRANCH REPRESENTATIONS WITH DYNAMIC GATED FUSION AND TRIPLE-GRANULARE ALIGN- MENT FOR DEEP MULTI- VIEW CLUSTERING
Wenyuan Kong
Zhibin Gu"
Independent Researcher
College of Computer and Cyber Security Hebei Normal University. Shijiazhuang, China
Beijing, China Kongwenyuan97 (e com
guzhibinghebtu, eduu en
Bing Li
School of Computer Science, China University of Labor Relations, Beijing, China State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation Chinese Academy of Sciences, Beijing, China bli@nlpr. ia aa.ac.ca an.cn
ABSTRACT
Multi-view clustering seeks to exploit complementary information across differ- ent views to enhance clustering performance, where both semantic and structural information erucial. However, existing approaches often bias toward type information while treating the other as auxiliary, overlooking that the reliability of these signals may vary across datasets and that semantic and structural cues can provide complementary and parallel guidance. As a result, such methods may face limitations in generalization and suboptimal clustering performance. To address these issues, we propose a novel method, Dual-branch Representations with dy- namic gatEd fusion and Anularity alignMent (DREAM), for deep multi- view clustering. Specifically, DREAM disentangles semantic information via a Variational Autoencoder (VAE) branch, while simultaneously captures structure- aware features through Graph Convolutional Network (GCN) branch. The re- sulting representations are dynamically integrated using a gated fusion module that leverages structural cues complementary guidance, adaptively balancing semantic and structural contributions to produce clustering-orienter latent em- beddings. To further improve robustness and discriminability, we introduce a triple-granularity feature alignment mechanism that enforces consistency across views, within individual samples, and intra-cluster. thereby preserving semantic- structural coherence while enhancing inter-cluster separability. Extensive experi- ments on benchmark datasets demonstrate that DREAM significantly outperforms SOTA approaches, highlighting the effectiveness of disentangled dual-branch en- coding, adaptive gated fusion, and
feature alignment.
INTRODUCTION
Recent advances in sensing and Internet technologies have enabled the collection of data from mul- tiple sources, offering diverse and complementary information about the same phenomenon (Fang al. 2023 Zhou et al. 2024). For example, rasterized high-definition map and LiDAR data pro- vide distinct yet complementary views for autonomous driving (Fadadu et al., 2022). Multi-view clustering (MVC), which aims to exploit both the shared and complementary information across views to uncover the underlying pattern of samples, has therefore emerged as a crucial paradigm for analyzing complex multi-modal data. In recent years, MVC has achieved remarkable success across domains, such as computer vision (Wang et al. 2024), biomedicine (Rappoport & Shamir 2018)
"Corresponding author.

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Published as a conference paper at ICLR 2026
and social interactions YYang al. 2014), where clustering performance has been substantially improved by integrating heterogeneous perspectives.
Depending on the underlying learning paradigms, MVC methods can be broadly categorized into conventional (shallow) and deep learning- based approaches. Conventional methods, including non- negative matrix factorization (NMF) (Liu et al. 2013), multi-kernel clustering (Zhang al.] 2024),
subspace learning (Zhang et al. 2017) and graph-based clustering (Lin & Kang] 2021). typically rely on linear assumptions and handcrafted features, limiting their ability capture complex pat- terns in high-dimensional data. In contrast, deep MVC methods possess the capability to model complex nonlinear relationships and high-dimensional patterns, thus attracting increasing attention. This capability has been instantiated in several representative paradigms, such as autoencoder- -bssed frameworks (Xu et al. 2022a Du et al., 202T), which reconstruct each view to capture rich seman- tic information; graph neural network-based approaches (Fan et al., 2020 Ling et al., 2023) which use graph topology as guidance to fuse attributes of each node and its neighbors, thereby generat- ing structure-aware representations; and contrastive learning- -baied methods (Lin et al. 2022b Xu
cct al. 2022b), which maximize mutual information across views to enforce cross-view consistency and improve cluster separability.
Despite differences in technical implementations, existing deep MVC methods consistently ac- knowledge that both semantic information (intrinsic sample features) and structural information (inter-sample relationships) are essential. However, most approaches emphasize one while treating the other as auxiliary, leading to imbalanced integration- for example, some prioritize constructing and utilizing consensus graphs with semantic embeddings as input (Ren et al. 2024 Du et al. 2023), while others focus on semantic reconstruction with structural information serving as guidance (Dong iet al. 2025). Consequently, semanties and structure are not jointly and equitably modeled, leaving room for explicitly disentangling and adaptively fusing both sources of information. Beyond the disentanglement, the fusion of semantics and structure poses another critical challenge. Naïve fea- ture concatenation can introduce conflicts, as features from different views, whether semantic or structural, often vary in informativeness, with certain views dominated by redundancy or noise, and their relative contributions may be datasct-dependent. Moreover, prior work typically aligns fea- tures at only one two levels-such cross-view consistency intra-cluster compactness- neglecting simultaneous multi-granulann ters, potentially leading to suboptimal clustering and insufficient preservation of remantic-structure coherence, particularly given the heterogeneous distributions of semantic and structural embeddings.
alignment across views, within samples, and among clus-
To address these issues, we propose Dual-branch Representations with dynamic gatEd fusion and alignMent model (DREAM), for deep MVC. Specifically, DREAM employs two dedicated encoders: a VAE encoder for semantic abstraction that captures intrinsic sample content, and a GCN encoder for structure-aware modeling that preserves inter-sample relations. Then, the extracted representations are dynamically integrated using gated fusion module which adaptively balances semantic and structural contributions within views and leverages structural cues as com- plementary guidance to fuse embeddings across views, producing muustering-orientte embeddings. Finally, to further improve robustness and discriminability, we introduce a rripl-grannllartyy feature alignment mechanism that enforces consistency across views, within individual samples, and intra- cluster, thereby preserving semantic-structural coherence while enhancing inter-cluster separability. Our main contributions are summarized as follows:
We design dual-branch disentanglement module that explicitly separates semantic and struc- tural information via dedicated semantics (VAE) and structure (GCN) encoders, enabling the model to capture heterogeneous information in complementary manner.
We propose an adaptive gated fusion module that treats semantic and structural embeddings as parallel information sources, dynamically balancing their contributions while suppressing re- dundant or noisy signals, thereby producing compact and discriminative latent representations.
We introduce unified feature alignment strategy that enforces alignment three granularitics-crovn crowsvvieww consistency, intra-sample coherence, and inter-cluster separabil- ity- ity-strengthening latent feature alignment and enhancing clustering discrimination. Extensive experiments on multiple datasets demonstrate that DREAM outperforms SOTA methods, validating the effectiveness of the dual-branch disentanglement, adaptive fusion, and triple-granularity feature alignment mechanisms for multi-view clustering.
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X
Sute
Gaze
Gare
Cluster
W1.90
WL
MI
Aigeerane
lact Sunahh Sunnfs Algunaal
Fiter - Cluster Algrovent
Figure 1: Framework of DREAM. Multi-view data are first encoded into semantic and structure- features, which then dynamically integrated via gated fusion module with triple- granularity alignment to produce clustering-orienter embeddings, subsequently used for clustering.
RELATED WORK
Multi-view clustering has attracted considerable attention for integrating complementary informa- tion across views, and can be broadly categorized into semantics-oriented methods, which exploit intrinsic sample attributes, and structure-oriented methods, which utilize inter-sample relationships.
Gemantics-Orientee Methods. common line of work in MVC emphasizes capturing rich seman- tic content by constructing latent embeddings, semantics encode shared attributes that distinguish instances for clustering. For example, Wang et al. (2022) proposed deep learning framework for MVC that factorizes view-specific data via NMF and aligns the resulting embeddings capture consistent semantics across heterogeneous views. Lin et al. (2022a) aligned sample representations across views contrastive way for binary clustering tree decoding. Xu al. (2022a) applied deep autoencoders to learn view-specific embeddings independently and concatenated them into global features to mitigate the negative impact of unclear clustering patterns individual views. Simi- larly, Zeng al. (2023) demonstrated that different views share invariant semantic distribution, enabling the model to reduce cross-view discrepancies and learn unified semantic representations without paired samples. [ii al. (2023) proposed dual mutual information constrained clustering method that minimizes the mutual information across all the dimensionalities to reduce the redun- dancy among features and maximizes the mutual information of the similar instance pairs obtain more unbiased and robust representations. Liu et al. (2024) extracted view-specific features, inte- grated them according view importance, and leveraged semantic features from both individual and fused views to generate cluster-fieeddl) features via two dedicated contrastive losses.
Oriented Methods. Other approaches often prioritize capturing inter-sample structural relationships explicitly, which reveal the relative arrangement of samples and inform clustering, For instance, Xue et al. (2021) combined adaptive graph learning with graph convolution and multiple kernel clustering to integrate global and local structures for clustering. Pan & Kang (202T) filtered noisy topological information and applied graph contrastive loss to learn a consensus graph. Yan et al. (2023) aggregated features across samples and views and enforced structure- guieeedggdddddddee contrastive learning for more discriminative representations. Similarly, Wang & Feng (2024) modeled structural view affinity matrix enhance clustering compactness. Cui et al. (2026) leveraged local neighborhood graphs and Gaussian modeling to capture latent structural information, improving cross-view consistency and intra-cluster compactness.
relations and constructed a consistent

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(2) and h are first fused via learned gating strategy, and the resulting latent representations gi (v) then aggregated across Published as a conference paper at ICLR 2026
In summary, semantics-oriented methods aim to achieve cross-view consistency in latent semantic spaces, while snuettrr--rientted methods emphasize inter-sample relations via topological informa- tion. However, existing approaches often treat one type of information primary and rely one or two types of contrastive techniques to enforce cross-view agreement and enhance inter-class sep- arability, leaving explicit disentanglement and adaptive balance of semantic and structural features, as well as aligning heterogeneous embedding spaces, underexplored. To address this, we propose a framework consisting of dual-branch module for semantics and structure encoding, a gated fusion mechanism to capture heterogeneous information and to dynamically balance their contributions, and saple-granulartty contrastive objective that enforces cross-view consistency, untra-sample co- herence, and inter-class separability.
METHOD
In this section, we first introduce the problem definition and provide an overview of DREAM's framework. We then describe the modules DREAM in detail: the Dual Branch Encoding Module, the Gated Feature Fusion Module, the Feature Alignment Module, and the Clustering Module.
3.1 PROBLEM DEFINITION AND FRAMEWORK OVERVIEW
Let a multi-view dataset be represented as X = (x xx,...,,XX where V denotes the (w)
(v) number of views, and (() {xx") Xx2 .....Xy'' E Nxd' represents the N samples with feature dimensionality d( in the e-th view. The goal of multi-view clustering is to partition the
samples into K clusters based on their multi-view features, without access to ground-truth labels.
The overall framework of the proposed DREAM model is depicted in Figure I First, the multi-view dataset X' is processed by the Semantics Encoding Branch (the upper left module with grey back- ground) and Structure Encoding Branch (the middle left module with grey background) separately, obtaining latent feature M(O) and H()), Second, for each sample i, latent feature (*) and h (() processed by the Gated Feature Fusion Module (the module with light orange background) to obtain the fused latent feature 1, Specifically, for each view v, μ
views using cross-view weighting cues derived from h to obtain the final fused feature 1, Third, I; is processed by the clustering module (the bottom right part in the module with orange back- ground), obtaining clustering result. Fourth, the module with light blue background comprises three alignment strategies corresponding to the first, second, and third step. The Cross- View Alignment module encourages each view of sample i to capture more consistent semantic and structural infor- mation. The Intra-Sample Alignment module encourages the fused embedding to remain close to
its semantic and structure-awe counterparts (μ!) (e) and ".").. Finally, the Inter-Cluster Alignment module enhances the discriminability among different clusters.
3.2 DUAL BRANCH ENCODING MODULE
Semantic and structural features are both essential for effective multi-view clustering. However, existing methods often exhibit bias toward one type of information while treating the other as auxiliary, thereby neglecting the variability in their relative reliability across datasets as well as the inherently complementary nature of semantics and structure. Such limitations may result in re- duced generalization ability and suboptimal clustering performance. To overcome these challenges, we design a Dual Branch Encoding Module that captures semantic and structural features simultane- ously and separately. Specifically, the semantics encoding branch employs variational autoencoder (VAE) to independently extract sample-level semantic content, while the structure encoding branch leverages a graph convolutional network (GCN) encoder obtain structure-aware embeddings by explicitly modeling inter-sample relationships.
Semanties Encoding Branch. For each view v. the semantic branch employs a VAE encoder, which takes the input data (+) and produces the mean (( = {M1 ./2 "...,
u (
and log-
arithm of the variance g(#) (log o1 2(0) log 2(v)
logo2(0)
log 2(0)) of the latent distribution via
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M = - (X tic feature representation. To ensure that M captures sufficient information from X(") and that the latent space follows standard normal distribution, this branch optimizes reconstruction loss Lyccon (Eq. I and Kullback-Leibler (KL) divergence Semontics (Eq. 2
Encoder (x))). The mean embedding ( is subsequently adopted as the seman-
LKL
V Lrecon
X 1
(1)
N
0=1
V
N
d
L
Semantics
(1 +++1oge log(o 2(00)) -M4j 0ij
2(0) 2(w)
(2)
KL
2
N 0=1
where (v) is the reconstructed feature computed by X = Dieanerr (M + exp (0.55(v)) oc). The overall loss of the semantic branch, denoted as LSemantiess is defined as
LSemantice = Lereonn + A1 Semantics
(3)
where Å is a weighting factor that balances the reconstruction and regularization terms.
Structure Encoding Branch. For each view
first build the graph structure among samples, where top-k similar samples are interconnected, Please refer to Appendix AA. graph construction method. Then, the GCN encoder updates each sample to explieitly merge graph structure into it
by H()) (1) (1) D()) ((),, where (n) {a.
E R NxN the adjacency matrix (x)
built in the first step. and ( is the degree matrix with d = Σ, ao)) To ensure that the learned embeddings preserve the original graph connectivity, we employ graph reconstruction loss. Specifically; the predicted adjacency matrix is defined (w) (HH() ), where (.) denotes the element-wise sigmoid function. The graph reconstruction loss Lsqqueture is then computed as the mean squared error between the predicted and ground-truth adjacency:
V
Lstructure
|
(
(4)
Overall Encoding Loss. Combining semantic and structural branches, the overall encoding loss is:
Lenoode =
LSemantics + Lstrrctuure
(5)
3.3 GATED FEATURE FUSION MODULE
While semantic and structural features are disentangled, fusing them remains challenging. Simple concatenation may suffer from heterogeneous feature distributions and redundancy or noise dom- inance. To address this, we propose Gated Feature Fusion Module that dynamically balances semantic and structural contributions, yielding compact and discriminative embeddings. It employs Intra-Veww Gating to fuse two embedded features within view, Cross-Vier Weighting to learn view importance and Cross-Veew Weighted Fusion to fuse views within each sample.
View Gating. Semantic and structure-aware embeddings within each view are first fused using a learnable gate:
(+)
(v) O a
(w) Gate (*) [n + b o (1 o (W Gatele [m (h ("))),
(6)
where 14; and h (0) denote the semantic and structure-aware embeddings for sample i, [-II-] rep- resents concatenation, (v)
Cate is a learnable linear projection, and (-)) is the sigmoid activation function. This operation adaptively balances semantic and structural information within each view.
Cross- View Weighting. Then, for each view e and sample i, the structure-aware embedding h; (v)
mapped scalar weight a; (v).
0 (v)
5 -We (h(()) E IR.
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where (w) is MLP with ReLU activation. Structure-aware embeddings H()) characterize sample- neighbor relations within each view, so that h encodes how strongly sample connects under that view. Projecting h into scalar weight og the model enables cross-view comparison of struc- tural coherence and adaptively emphasizes views providing more reliable inter-sample relationship
cues.
Cross-Vies Weighted Fusion. Finally, gated embeddings g (v) are fused across views using the normalized weights (1),
V
exp(a (0)) E exp(a.")) ("))""
4 =
(8)
where a is obtained via softmax normalization across views for each sample. The final fused representation incorporates both semantic and structure-aware information, and captures comple- mentary cues across multiple views.
3.4 FEATURE ALIGNMENT MODULE
In multi-view learning, decoupled and fused semantic and structure- features may still be inconsistent across branches and views, and fused embeddings may lose critical information or dis- criminability. To address this, introduce the Feature Alignment Module, which enforces align- ment at multiple levels to produce robust and informative clustering-oriented representations.
Cross-View Alignment. To reduce discrepancies between views, embeddings from all views are aligned toward shared consensus using cross-view distillation losses for both the Semantics En- coding Branch and the Structure Encoding Branch:
I Semantics distill
Σ
[M M
distilll
I
Structure
=
Σ N2 A
A
where M and A denote the consensus targets obtained by aggregating the semantic and structure embeddings across all views. These losses encourage each view capture consistent semantic and structural information, facilitating more coherent fused representations.
Intra-Sample Alignment. preserve key semantic and structural information for each sample and maintain global discriminability across samples, a triplet-style InfoNCE loss is employed:
V
N
exp(sim(L, μ4 (0) //T) exp(sim(I, h, )/7)
1
Tintra
log
(10)
Σド=1
" exp(sim (1) )///)))))))))))))(()) exp(sim(1, /T))
1=1
where sim(:,-) denotes cosine similarity, and is the temperature parameter. More specifically, for each sample i, the sara-Sample Alignment loss encourages the fused embedding 1 to remain close to its semantic and structure aware counterparts, i.c., (1;, p )) and (I, (((")) in the numerator, while simultaneously reduces the similarity between 1 and semantic and structure- embeddings from other samples, i.e., (li, (v)) and (I,, h())) in the denominator.
Inter-Cluster Alignment. To enhance the discriminability among different clusters, a triplet loss which imposes two complementary forces an attractive force that pulls the anchor and its positives toward each other to enforce intra-cluster compactness, and repulsive force which pushes the anchor and its negatives away from each other ensure inter-cluster separation, is applied over the fused embeddings:
Linter R
Σ max (0. 1|, 1pl12 ||la 1, + m)) (a.p.)ES
(11)
where (a, p. n) denotes a triplet of anchor, positive, and negative samples, m is the margin, S is the set of selected triplets, and R is the total number of selected triplets. More specifically, for each triplet (a, p, n2), the anchor (a) refers to a sample currently used as the basis to construct the triplet; the positive (p) is another sample whose pseudo label is identical to that of the anchor,

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meaning the two predicted to belong to the same cluster; the negative (n) is another sample whose pseudo label differs from that of the anchor. Pseudo labels are generated by the Clustering Module (Sec. 3.5) during training, where the arg max; Pit is used the pseudo label for sample updated every 3 epochs. Using pseudo labels provides dynamic refinement of cluster structure and training stability without propagating noisy signal from early-stage noisy assignments thus leading to stable convergence.
Overall Feature Alignment Loss. The overall feature alignment loss is formulated by integrating the cross-view, intra-sample, and inter-cluster alignment objectives:
Semantics + A distit Aligg distiit
+ Lintra + Linters
(12)
where A2 is a constant number set to 10 during experiment.
3.5 CLUSTERING MODULE
Finally, clustering layer is adopted to obtain cluster assignments for fused representation 4. This layer maintains a set of trainable cluster centers {cx [c where K denotes the number clusters.
The similarity between latent representation 4, and each cluster center is first measured using Stu- to provide a soft assignment of sample to - lIgTTT
dent's r-distribution kernel qik , (I+IIL
each cluster, where ga represents soft assignment distribution of sample to cluster k. Before com- puting distances, both I; and C₁ Io-normali to improve numerical stability, Then, sharpened
1/sem
1/tem where tem the temperature param- target distribution p, is generated through Piu Σ
eter controlling the sharpness of the distribution. Finally, each sample is assigned to the cluster with the highest probability Vpeed arg max; Pik-
During this process, entropy loss Lentropy (Eq, encouraging each sample's soft assignment to be confident (i.e., close to one-hot), and KL divergence LKL. Cluster (Eq. 14 aligning the predicted soft assignments q with more confident target distribution p to enhance cluster purity, are leveraged.
1
Lentropy
qik log(gi + +E),
(13)
N
N
K
L Cluster
KL
=
(T((pl))
N
ΣΣ
Pik log
Pik Pik + C
(14)
k=1
where is small constant to avoid numerical issues. Overall Cluster Loss. The overall cluster loss is:
LCluster Lentropy 0g LKL.
Cluster
(15)
where Ag is used to balance the two losses.
3.6 THE OVERALL OPTIMIZATION OBJECTIVE
By jointly considering the Dual Branch Encoding Module, the Gated Feature Fusion Module, the Feature Alignment Module, and the Clustering Module, the overall objective function of the DREAM model can formulated as:
T LEncode + oTL.tigg 3T..usttr
(16)
where and / are byperparameters that adjust the contributions of three losses. To make clear presentation, the algorithm flow shown Algorithm
EXPERIMENT
4.1 EXPERIMENTAL SETTINGS
Datasets: Six widely used multi-view datasets-Yae NGS, BBC, UCL, HW, and ALOI100 are employed for clustering experiments. Details of these datasets summarized in Table

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Published as a conference paper at ICLR 2026
Algorithm The optimization of DREAM
Require: Multi-view dataset (x (o) v m= Cluster number K
1: Initialize graph structures {A (
] J==1 and cluster centers {ck [ck]
2: while Not reach the maximum iteration Tmax do
3:
for each view V = 1 to V do
4: Extract semantic features M(r) and structure features Hv) (Sec. 3.2)
5: Calculate Encoding Loss (Eq.
6: end for
7:
Fuse M and ((")
(1) (Sec. 3.3 and calculate Feature Alignment Loss (Sec. 3.4 Eq. 12
8: Perform clustering (Sec. 3.5) and calculate Cluster Loss (Sec. 3.5 Eq.
15)
9:
Jointly optimize the overall objective function Lrotal (Eq. 16
Backpropagate and update model parameters
11: end while
12: Return: The final clustering result
Comparison Methods: Eight representative SOTA MVC methods are used for comparison, cluding DSMVC (Tang & Liu 2022). MFLVC (Xu et al. 2022b), SEM (Xu et al. (023). GCFAg-
gMVC Yan et al. 2023). SCMVC (Wu et al. 2024). MVCAN (Xu et al. 2024), SCM (Luo et al.
2024.. and GDMVC (Bai al. 2024).
Evaluation Metrics: Clustering performance is evalu-
Table 1: Summary of datasets used for clustering experiments.
ated using three standard metrics: clustering accuracy (ACC), normalized mutual information (NMI), and pu- rity. Higher metric values indicate better performance.
Dataset
Samples
Views
Clusters
Yale
165
3
15
Implementation Details: Our model implemented in desktop equipped with
NGS
500
3
5
PyTorch 2.7.1 and trained an NVIDIA GeForce RTX 5070 Ti GPU and 64 GB of RAM, using the Adam optimizer with default settings. Given the variations in sample sizes, feature dimensions, and the number of views across datasets, hyperparame- ters are tuned for each dataset from a candidate range obtain optimal configurations. The learning rate used for different datasets ranges between [0.1, O. 0.00005). For baseline methods, we use hyper- parameters recommended in their papers released codes and perform light search around these defaults to prevent performance loss due to mismatched settings and report the best result.
BBC
685
4
5
UCI
2000
3
10
HW
2000
6
10
ALO1100
10800
4
100
4.2 COMPARISON RESULTS
Table reports the experimental performance our DREAM model and eight baseline methods across different datasets. The best performance for each metric is highlighted in bold, while the second-best is indicated with underlining. As shown in Table 2 different methods exhibit vary- ing performance across datasets. Our method, DREAM, consistently outperforms all baselines on six benchmark datasets and three evaluation metrics, demonstrating its strong generalization ability and robustness. For instance, on the ALOI100 dataset. DREAM surpasses the second-best method, GDMVC, by 5.19%. 4.22%, and 5.93% in ACC, NMI, and Purity, respectively. This improvement clearly validates the effectiveness of the disentangled dual-branch encoding, the adaptive gated fu- sion and the viple-granularie alignment in enhancing multi-view clustering performance. Please see further discussion in Appendix A.7
4.3 ABLATION STUDIES
To clearly illustrate the contribution of each core component in DREAM, we conduct a systematic ablation study by removing the Semanties Encoding Module, Strueture Encoding Module, Gated Feature Fusion Module, and Feature Alignment Module individually. Table reports the perfor- mance under each ablation setting.
First, the two encoding branches are examined. Removing the Semantics Encoding Module leads consistent drop in performance across datasets, indicating that semantic representations provide
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Table 2: Performance comparison multi-view clustering algorithms on six benchmark datasets.
Datascts
DSMVC MFLVC SEM GCFAgg SCCMVC SCMVC MVCANSCM
GDMVC Ours
ACC
Yale
64.85
21.82
27.27
30.91
39.39
40.60
49.09
76.97
78.18
NGS
40.00
90.40
93.80
88.60
93.20
30.60
97.20
40.20
97.80
BBC
42.48
77.81
61.46
58.98
86.57
78.54
58.98
34.89
90.07
UCI
93.75
86.05
87.05
83.20
68.65
92.00
67.45
85.50
95.90
HW
95.85
68.40
81.35
81.25
82.40
95.10
84.25
88.25
97.80
ALOI100
15.66
7.06
65.81
4.85
4.34
67.98
3.96
81.81
87.00
NMI
Yale
66.81
19.48
35.11
32.84
40.37
45,48
51.92
76.79
79.87
NGS
12.26
76.05
81.89
74.70
82.20
17.51
91.03
12.19
92.90
BBC
59.20
44.12
53.26
71.53
63.26
31.39
4.85
72.75
UCI
89.24
79.00
76.72
72.95
65.88
85.23
60.20
86.15
92.01
HW
92.21
66.68
71.58
72.67
73.53
89.75
73.41
89.37
95.05
ALOI100
40.69
37.90
82.97
14.80
12.47
83.76
11.47
86.66
90.88
Purity
Yale
65.45
21.82
27.88
5.44
40.61
43.03
52.12
76.97
82.42
NGS
41.40
90.40
93.80
88.60
93.20
33.40
97.20
40.60
98.00
BBC
44.96
77.81
65.99
68.91
86.57
78.54
58.98
37.81
90.07
UCI
93.75
86.05
87.05
83.20
72.10
92.00
68.80
85.50
96.30
HW
95.85
68.55
81.35
81.25
82.40
95.10
84.25
88.25
97.80
ALOI100
16.36
7.06
68.61
5.20
4.51
72.17
4.11
82.25
88.18
Table 3: Ablation studies on the contributions of each component in the DREAM model.
Datascts
UCI
HW
ALOI100
Metrics
ACC
NMI
Purity
ACC
NMI
Purity
ACC
NMI
Purity
w/o Semantics Encoding
87.05
80.44
87.05
90.55
87.00
92.05
84.68
89.24
86.69
w/o Structure Encoding
75.90
67.85
78.40
84.65
86.30
94.50
78.29
87.32
82.16
Gated Fusion
82.70
83.44
85.70
92.85
87.82
94.00
83.90
89.73
85.07
w/o Feature Alignment
88.35
81.58
89.75
96.25
91.83
96.25
86.62
90.61
87.84
Our model
95.90
92.01
96.30
97.80
95.05
97.80
87.00
90.88
88.18
indispensable, discriminative cues for clustering. Removing the Structure Encoding Module results in the most severe performance degradation- for example, ACC decreases by 20% on the UCI datasett-demonstraatin that inter-sample structural relations are fundamental for reliable multi-view clustering. Next, the importance of the fusion mechanism is assessed by replacing the Gated Feature Fusion Module with simple averaging. While the changed model occasionally surpasses single- branch variants, remains substantially inferior the full DREAM model. This indicates that naive averaging fails to effectively leverage the complementary information semantic and structural embeddings, whereas gated fusion adaptively balances view informativeness. Finally, disabling the Feature Alignment Module also results in noticeable performance degradation. On the UCI dataset, ACC decreases from 95.909 88.35%, demonstrating that triple-granularity alignment strengthens cluster cohesion by harmonizing representations across views, samples, and clusters.
Overall, the ablation results validate that each mod- ule contributes meaningfully and that the integration of dual-branch encoding, gated feature fusion, and feature alignment achieves superior multi-vies view clus- tering performance.
4.4 SENSITIVITY ANALYSIS
We conducted a sensitivity analysis on the hy- perparameters of DREAM (Figure 2) Specifi- cally, we investigated two key hyperparameters, a and B, by varying their values across the range
(a) BBC
(b) HW
Figure 2: Impact of varying the hyperparam- eters and / clustering performance.

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[0.001, 0.01, 0.1, 10, 100, 1000]. Results show that changes in these parameters induce only minor fluctuations in performance on the BBC and HW datasets, indicating that our method is highly robust to hyperparameter selection. Additional results other datasets are provided in Appendix A.5
4.5 CONVERGENCE ANALYSIS
We ploted metrics and losses over training iterations with average (avg) and standard deviation (std) from five random-seed experiments on BBC and HW (Figure 3) to demonstrate the robustness of model. Results show that metrics and losses stabilize with the training cycle, indicating that the model shows good convergence properties.
Cock
Cock
Dock
Figure 3: Convergence analysis on BBC and HW.
4.6 VISUALIZATION
To qualitatively assess the clustering capability of DREAM, we performed t-SNE visualizations on both the raw features and the fused features learned by DREAM on the BBC and HW datasets (Figure 4. Compared with the raw features, the learned fused features exhibit a markedly clearer separation of clusters, indicating that DREAM effectively captures highly discriminative represen- tations that are well-suited for clustering tasks.
(a) BBC raw features
BBC fused features
HW features
(d) HW fused features
Figure 4: t-SNE visualization raw and learned fused features on BBC and HW datasets.
CONCLUSION
In this work, we present DREAM, novel multi-view clustering framework designed disentan- gle and integrate semantic and structural information to improve clustering performance. DREAM introduces three innovative components: a dual-branch encoder that separately models semantic and structure-aware representations, gated fusion module that adaptively balances contributions of representations, and a triple-granulase granularity alignment strategy that enforces consistency across views, within individual samples, and within clusters. Comprehensive experiments multiple benchmark datasets demonstrate that DREAM consistently surpasses SOTA methods, highlighting its effective- ness and generality for multi-view clustering.
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6 REPRODUCIBILITY STATEMENT
We have made efforts to ensure the reproducibility of our results. The details of method are provided in Sec. 3 and details of model architecture are described in Appendix, A.6 The code of model will be made publicly available upon publication.
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A APPENDIX
A.1 GRAPH CONSTRUCTION
Two graph structure initialization methods are leveraged in our periment for datasets of different kind. For image datasets, includ- ing ALO1100, HW, Yale and UCI, k-nearest neighbors obtained by computing Euclidean distance to con- struct graphs for each view feature matrix X()) This approach is ef- ficient for dense image features and leverages geometric distance which is natural for visual descriptors. For text datasets, including NGS and BBC,
Table 4: Fuclidean-distance graph construction methods on HW and BBC. For brevity, COS and EUC are used in the table to represent cosine-similarity and Euclidean-distance, respec- tively.
Comparison between cosine-similarity and
Datasets
ACC
NMI
Purity
COS
EUC
COS
EUC
COS
EUC
HW BBC
97.50
97.80
94.73
95.05
97.50
97.80
90.07
24.23
72.75
1.31
90.07
33.87
cosine-similarity- based k-nearest graph is built. Specifically, first, each view feature ( is L.2- normalized; next, the cosine similarity matrix is computed and its diagonal is set to zero to avoid self-loops; finally, the top-k largest similarity entries kept as neighbors, producing a graph. This procedure is robust for sparse while high- nigh-dimensional textual representations and explicitly controls local connectivity.
To further demonstrate the influence of graph construction strategies on model performance, we conducted an experiment comparing posine-similarty graphs and Euclidean-distance graphs under the same hyperparameter settings two representative datasets HW and BBC. The results are sum- marized in Table 4 can be observed that while modifying the graph construction strategy for HW leads to only slight performance drop, applying the same change to BBC results in a sub- stantial degradation. This indicates that text-based datasets, such as BBC, are highly sensitive to the graph construction method and particularly benefit from cosine- citiieiiiiiillitibaaiiieieie graph construction method.
A.2 RATIONALE FOR USING GCN ENCODER RATHER THAN GROUND-TRUTH ADIACENCY
To verify the necessity of the GCN encoder, we conducted an experiment which the GCN encoder is replaced by linear layer that directly receives the ground-truth adjacency matrix as input. The linear layer employed because the next module requires inputs of the same size. The results, summarized in Table show that while using the ground-truth adjacency achieves reasonable (good though not better) performance on image datasets (Yale, UCI, HW, and ALOI100). leads to a significant performance drop text-based datasets (NGS and BBC). These findings demonstrate that the GCN encoder is essential for effectively dealing with both image- and text-based datasets simultaneously.
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Table 5: Comparison between using GCN encoder and Ground-truth Adjacency.
Yale
NGS
BBC
UCI
HW
ALOI100
ACC
Ours (with GCN encoder)
78.18
97.80
90.07
95.90
97.80
87.00
Ours (with Ground-trus Adjacency)
77.58
56.60
51.24
92.90
94.50
81.19
NMI
Ours (with GCN encoder)
79.87
92.90
72.75
92.01
95.05
90.88
Ours (with Ground-truth Adjacency)
78.28
31.93
27.00
89.56
92.31
89.03
Purity
Ours (with GCN encoder)
82.42
98.00
90.07
96.30
97.80 97.15
88.18
Ours (with Ground-truth Adjacency)
80.61
61.20
59.56
95.25
84.85
A.3 RATIONALE FOR USING STRUCTURAL-CUES IN THE GATED FEATURE FUSION MODULE
The rationale for using structure- aware embedding H (o) guidance for cross-view weighting is twofold. First, as stated in Sec. 3.3 the structure-aware embedding h incorporates inter-instance information, thus hints the structural reliability of sample in view In other words, it captures how consistently this sample aligns with its local neighborhood that view's graph structure. This property provides valuable the structural cues with semantic cues conducted to verify the effectiveness of structural cue-aided gated feature fusion 6T.
for view weighting during fusion. Second, an experiment replacing
Table 6: Comparison between using structural cues and semantic cues the Gated Feature Fusion Module.
Yale
NGS
BBC
UCI
HW
ALOT100
ACC
Ours (with structural-guide fusion) Ours (with semantie-guided fusion)
78.18
97.80
90.07
95.90
97.80
87.00
76.97
86.00
87.74
93.85
97.55
85.99
NMI
Ours (with structural-guided fusion)
79.87
92.90
72.75
92.01
95.05
90.88
Ours (with semantic-guided fusion)
76.60
73.04
68.25
89.75
94.39
90.86
Purity
Ours (with structural-guided fusion)
82.42
98.00
90.07
96.30
97.80
88.18
Ours (with ammantic-guided fusion)
80.00
88.40
87.74
95.10
97.55
89.56
A.4 ABLATION STUDIES
Experiment reuslts on NGS, Yale and BBC datasets are reported in Table 7 from which we can that all the modules in our model are verified indispensable. To be specific, removing ei- ther the semantic or structural encoding module leads to clear performance degradation, with the structural branch being especially critical. Disabling the gated fusion module also reduces accuracy, confirming that simple averaging cannot fully exploit semantic-strucdt complementarity. Finally, the feature alignment module essential for maintaining representation consistency, as removal noticeably lowers performance.
Interestingly, the BBC dataset shows different trends from NGS and Yale. When Semantics En- coding Module is removed, the model achieves even higher accuracy than the full model version. This is likely because BBC inherently a text-based dataset whose raw views already carry strong and highly correlated semantic signals. Adding explicit Semantics Encoding Module may rather introduce redundancy or noise, leading to inferior performance. Instead, the scarce structural infor- mation in the data becomes relatively more valuable. These results highlight that the relative
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importance of semantic and structural information is dataset-dependent, further validating the ne- cessity of our disentangled design.
Table Ablation studies on NGS, Yale and BBC datasets.
Datasets
NGS
Yale
BBC
Metrics
ACC
NMI
Purity
ACC
NMI
Purity
ACC
NMI
Purity
w/o Semantics Encoding
95,20
87.56
95.20
72.12
72.82
76.36
91.53
76.72
91.53
w/o Structure Encoding
45.60
19.74
45.60
67.88
68.37
69.09
29.20
3.48
35.33
w/o Gated Feature Fusion
96.60
91.18
97.00
73.94
73.45
76.97
82.34
63.77
83.65
w/o Feature Alignment
62.40
50.08
68.8 68.80
74.55
77.63
81.21
75.62
47.42
75.62
Our model
97.80
92.90
98.00
78.18
79.87
82.42
90.07
72.75
90.07
A.5 SENSITIVITY STUDIES
Two hyperparameters, a and 8, are varied across the range |0.001, 0.01, 0.1, 1, 10, 100, 1000] to examine their impact on model performance. Results (Figure show that varying these parameters causes only minor performance variations across the studied datasets, confirming the robustness of our approach.
(a) NGS
(b) Yale
(c) ALOT100
(d) UCI
Figure 5: Sensitivity analysis of hyperparameters a and 3 on four datasets.
A.6 DETAILS OF MODEL ARCHITECTURE
Dual Branch Encoding Module. The semantic encoder our model consists of three linear layers with ReLU activations and an additional linear layer yielding the mean and log- lor-variance for the latent semantic representation. The structural encoder composed of three graph convolutional layers with ReLU activations.
Gated Feature Fusion Module. It leverages one linear layer with sigmoid activation to adaptively fuse embeddings from two encoders within each view, and another two linear layers with ReLU activation in between to learn view importance.
Feature Alignment Module. This module mainly loss functions defined in Sec. 3.4 align features at three granularities. It contains no trainable layers.
Clustering Module. This module initializes trainable cluster centers using K-means the fused features, and iteratively refines them via soft assignment.
A.7 FURTHER COMPARISON BETWEEN DREAM AND EXISTING DEEP LEARNING-BAve APPROACHES
While existing methods may struggle to achieve satisfactory clustering results for small-scale datasets (e.g., Yale, BBC) and datasets with a large number fine-grained classes (e.g., ALOI100), DREAM achieves significant clustering improvement for these datasets. We believe that the diffi- culty faced by existing methods on these tasks primarily stems from representation unreliability and insufficient class spesifi information. When only small number of samples available per class, is difficult for the model to extract enough discriminative cues, and the learned embeddings may contain misleading signals lack informative structure, ultimately leading suboptimal clustering
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performance. Our method achieves improvements these challenging datasets for the following two reasons. First, mual-branch disentanglement with adaptive feature fusion enhances information richness and representation reliability. Unlike methods that extract single type of information and emhed it into one latent space, our framework explicitly disentangles semantic information (via the VAE branch) and structural information (via the GCN branch), and integrates them through gated fusion mechanism. This design not only compels the model to learn complementary perspectives of the data, thereby enriching information diversity, but also enables dynamically balance the semantic and structural contributions according to the informativeness of each, thereby improving the reliability of the learned representations. Second, triplet-aligmment improves robustness against fine-grained noise. Datasets such ALOI100 contain fine-grained categories, leading to subtle inter-class differences. Consequently, representations are more susceptible to noise. Our alignment mechanism jointly aligns latent spaces across views, across information types, and within clusters, foreing the model to aggregate information from multiple sources, maintaining consistent represen- tations while mitigating noisc.
A.8 THE USE OF LARGE LANGUAGE. MODELS (LLMS)
The language of this manuscript was refined with the assistance of a large language model (LLM). The LLM was also consulted during the early idea-formation stage assist in reviewing relevant literature. All other parts of this paper, including experiments, analyses, and conclusions, were designed and conducted solely by the authors.
16


Reviews:
[{"review_id": "wBRoToaVRy", "reviewer": "Reviewer_hicV", "summary": "The paper develops a multi-view clustering framework based on a dual-branch representation that simultaneously captures semantic and structural information from multiple views. It employs a gated fusion module that adaptively balances the contributions of semantic and structural features according to the characteristics of the data, producing latent representations more suitable for clustering. In addition, the authors introduce a triple-granularity feature alignment mechanism to enforce consistency at three levels. This design enhances the coherence between semantic and structural information while improving inter-cluster separability.", "questions": "Please see the Weaknesses section.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1.The developed framework systematically tackles imbalanced integration of semantic and structural information, conflicts in feature fusion, and limited feature alignment, providing a well-motivated solution to longstanding issues in deep MVC.\n2.The paper’s division of multi-view clustering methods into semantics- and structure-oriented categories is insightful, highlighting the need to jointly leverage both information types.", "weaknesses": "1.The rationale for using structural cues in the gated fusion module as complementary guidance for cross-view embedding fusion is unclear. Clarification with semantic-guided fusion would be beneficial.\n2.Some acronyms, such as VAE and GCN, are introduced without explanation. Providing their full names on first mention would improve clarity.\n3.Certain modules in Figure 1 are not fully described, making it difficult to understand their exact functionality.\n4.Regarding the graph reconstruction loss, which minimizes the mean squared error between predicted and ground-truth adjacency matrices, it is not explained whether using the ground-truth adjacency directly would suffice. If so, the role of the GCN encoder requires further justification.\n5.Both Equation (3) and Equation (14) involve KL divergence, but the distinction between them is not clearly explained.\n6.In the experiments, it would be helpful to include references or links for the datasets used to enhance reproducibility.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JSdl9Dgo0y", "reviewer": "Reviewer_A3FV", "summary": "This work addresses the challenge of effectively integrating semantic and structural information in multi-view clustering, where existing methods often emphasize one type of information while neglecting the other. To tackle this, the authors propose a dual-branch design (a VAE branch for semantic representations and a GCN branch for structure-aware features) to disentangle the two types of information and adaptively fuse them via a gated mechanism. Additionally, a triple-granularity feature alignment strategy is introduced to enforce consistency across views, samples, and clusters, enabling the model to learn clustering-friendly feature representations and improve clustering performance.", "questions": "In Equation (5), the overall encoding loss combines semantic and structural components. Why are their contributions considered equally important? Shouldn’t there be a hyperparameter to balance them?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The proposed method introduces a novel feature disentanglement and cross-view fusion strategy, explicitly modeling the rich and complementary information in multi-view data.\n\n2. The introduction clearly motivates the proposed approach, and the cited references are representative and sufficiently comprehensive.\n\n3. The workflow diagram provided in the manuscript is clear and readable, aiding in the understanding of the method.", "weaknesses": "1. In Equation (4), the method for initializing the graph structures is not specified; it is unclear how the \"Initialize graph structures\" step is performed. Furthermore, the manuscript does not discuss how different graph construction methods affect the model’s performance.\n\n2. There are inconsistencies in the coefficients: Equation (1) uses $1/N$, whereas Equation (4) uses $1/N^2$, and the mechanism for determining these coefficients is not explained. A similar inconsistency appears in the two forms of Equation (9). Additionally, $N$ is not defined.\n\n3. The explanation of Equation (10) is unclear and difficult to understand.\n\n4. The presentation of the Ablation Studies section lacks clear organization and logical flow. It is recommended that the authors further refine and polish this section to improve readability and coherence.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zPSmJQ8eII", "reviewer": "Reviewer_yNg5", "summary": "The manuscript proposes a multi-view clustering method that explicitly leverages both semantic and structural representations. The approach first disentangles semantic and structure-aware features using a dual-branch architecture and then adaptively integrates them through a gated fusion mechanism. To further enhance robustness and inter-cluster separability, a triple-granularity feature alignment strategy is applied across views, samples, and clusters. The manuscript also presents extensive experimental results demonstrating the effectiveness of the proposed method.", "questions": "See Weakness.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The manuscript is well written, and the overall structure is logically organized.\n\n2. The proposed method effectively enhances performance by jointly exploring multi-view semantic and structural information, performing cross-view fusion, and introducing multi-level alignment strategies to fully exploit the rich information across views.\n\n3. The experimental section includes both performance comparisons and visualization analyses, which effectively validate the effectiveness of the proposed approach.", "weaknesses": "1. The flowchart suggests that pseudo-label information is incorporated into the framework. However, the manuscript does not clearly explain how these pseudo labels are generated or what specific role they play in the learning process. The authors should clarify the source of the pseudo labels and elaborate on how they contribute to model optimization and clustering performance.\n\n2. In the Inter-Cluster Alignment loss, the roles of (a, p, n) are not sufficiently clarified. Their specific definitions and underlying physical interpretations should be explicitly described.\n\n3. Apart from the balancing parameter in the model, the descriptions of other hyperparameter settings are insufficient. It remains unclear whether these parameters are kept consistent across datasets, how their values are determined, and whether the comparisons with baseline methods are conducted under fair and comparable conditions.\n\n4. The experimental section would benefit from a convergence analysis.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "RgFecv5VMO", "reviewer": "Reviewer_K17d", "summary": "The paper proposes DREAM, a deep multi-view clustering framework that jointly exploits semantic and structural information. DREAM disentangles semantic features via a VAE branch and captures structure-aware representations via a GCN branch, dynamically integrating them through a gated fusion mechanism that adaptively balances their contributions. Moreover, a triple-granularity alignment strategy enforces consistency across views, samples, and clusters, enhancing robustness and discriminability.", "questions": "See Weakness section.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper proposes a dual-branch disentanglement module that explicitly separates semantic and structural information via dedicated encoders—a VAE for semantics and a GCN for structure—allowing the model to capture heterogeneous information in a complementary manner. This is an interesting and potentially valuable idea.\n2. Experimental results on six benchmark datasets demonstrate that the proposed method outperforms other approaches.\n3. The ablation study verifies the effectiveness of each module in the proposed model.", "weaknesses": "1. Some parts of the model description are unclear; for example, the value of $\\lambda_1$ in Equation (3) is not provided.\n2. The Feature Alignment Module is one of the innovations of the paper; however, the description of the Overall Feature Alignment Loss is not sufficiently clear. In Equation (10), both $L_{\\text{Semantic}}$ and $L_{\\text{Structure}}$ share the same hyperparameter $\\lambda_2$, but it is unclear why they are set equal. In addition, $L_{\\text{intra}}$ and $L_{\\text{inter}}$ do not have any hyperparameters, and the rationale for this choice is not explained.\n3. The manuscript refers to a \"Cross-View Weighting\" mechanism within the Gated Feature Fusion Module. Nevertheless, it is not evident how the cross-view component is realized, as Equation (7) does not explicitly reflect any cross-view fusion process. The authors are encouraged to clarify this implementation detail.\n4. The manuscript contains formatting issues; for example, a line break occurs at lines 458--459.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)