Incomplete Multi-View Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation
Authors: Xu Yan
Jun Yin
Shiliang Sun
Minghua Wan
Venue: ICLR 2026 Poster (2026)
Keywords: multi-label classification
dual incomplete multi-view multi-label classification
representation learning
label correlations
multi-view consistent representation
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: Although multi-view multi-label learning has been extensively studied, research on the dual-missing scenario, where both views and labels are incomplete, remains largely unexplored. Existing methods mainly rely on contrastive learning or information bottleneck theory to learn consistent representations under missing-view conditions, but loss-based alignment without explicit structural constraints limits the ability to capture stable and discriminative shared semantics. To address this issue, we introduce a more structured mechanism for consistent representation learning: we learn discrete consistent representations through a multi-view shared codebook and cross-view reconstruction, which naturally align different views within the limited shared codebook embeddings and reduce feature redundancy. At the decision level, we design a weight estimation method that evaluates the ability of each view to preserve label correlation structures, assigning weights accordingly to enhance the quality of the fused prediction. In addition, we introduce a fused-teacher self-distillation framework, where the fused prediction guides the training of view-specific classifiers and feeds the global knowledge back into the single-view branches, thereby enhancing the generalization ability of the model under missing-label conditions. The effectiveness of our proposed method is thoroughly demonstrated through extensive comparative experiments with advanced methods on five benchmark datasets. Code is available at https://github.com/xuy11/SCSD.

Published as a conference paper at ICLR 2026
INCOMPLETE MULTI-VIEW MULTI-LABEL CLASSI- FICATION VIA SHARED CODEBOOK AND FUSED-
TEACHER SELF-DISTILLATION
Xu Yan', Jun
Yin';, Jun Shiliang Sun"; Minghua Wan'
Coollege of Information Engineering, Shanghai Maritime University Schooo of Automation and Intelligent Sensing, Shanghai Jiao Tong University yanxuêstu, shmtu. edu. cn, {junyin, (ahaan) @shmtu edu. cn shi shiliangsun@gmai1 com
ABSTRACT
Although multi-view multi-label learning has been extensively studied, research the dual-missing scenario, where both views and labels are incomplete, remains largely unexplored. Existing methods mainly rely on contrastive learning or infor- mation bottleneck theory to learn consistent representations under missing-view conditions, but loss-based alignment without explicit structural constraints lim- the ability capture stable and discriminative shared semantics. To address this issue, we introduce a more structured mechanism for consistent representa- tion learning: we learn discrete consistent representations through a multi-view shared codebook and cross-view reconstruction, which naturally align different views within the limited shared codebook embeddings and reduce feature redun- dancy. At the decision level, we design weight estimation method that eval- uates the ability each view to preserve label correlation structures, assigning weights accordingly enhance the quality of the fused prediction. In addition, we introduce a fused-teacher self-distillation framework, where the fused predic- tion guides the training of view-specific classifiers and feeds the global knowl- edge back into the single-view branches, thereby enhancing the generalization ability of the model under missing-labe labell conditions. The effectiveness of our pro- posed method is thoroughly demonstrated through extensive comparative exper- iments with advanced methods on five benchmark datasets. Code is available Ittps: / gi thub. com/ xuy 1/SCSD
INTRODUCTION
Multi-view data are very common in the real world (Zhao et al. 2017). where single sample often described by multiple representations from different modalities or various feature extrac- tion methods, such as RGB/HSV/GIST for images, audio-visual synchronization for videos, con- ten//ehaviorrsoccal views recommender systems, and multi-omics data in bioinformatics (Yan) et 2021). The goal of multi-view learning is to exploit the consistency and complementarity among views to improve the quality of representations and the performance of downstream tasks such classification. It has already become fundamental technique in numerous real-world appli- cations (Yu et al., 2025).
Similarly. many tasks naturally fall into the multi-labe setting, where single sample is often asso- ciated with multiple labels, such as in image classification and multi-topic text classification (Hang & Zhang 2021). Multi-label classification can improve prediction performance by exploiting la- bel correlations (Chen et al. 2019). such correlations are effectively modeled and utilized, they only alleviate the negative impact of label sparsity but also enhance prediction accuracy and robustness under limited annotation conditions.
However, the ideal assumption of complete multi-view data with fully observed multi-label anno- tations is rarely satisfied in practice (Wen et al. 2023). On the one hand, incomplete multi-view
"Coreeponding author.

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Published as a conference paper at ICLR 2026
data very common (Yn & Sun 2021). During multi-view data collection, sensor failures, occlu- sions, or cross-domes restrictions (c.g., privacy and authorization constraints) often render certain views unavailable during training inference. On the other hand, missing multi-label data are also prevalent (Chen et al. (020). This is mainly due to the high cost of fine-grained annotation and the limited attention of annotators, which often result in only partial labels being observed for some samples. Treating missing labels as negative instances in a naive way further aggravates the class imbalance problem and introduces bias (Ridnik al. 2021).
A more challenging scenario arises when both multi view and multi-label data are missing simul- taneously, forming the dual-missing situation (Liu et al. 2023b). Firstly, missing multi-view data affect the learning of consistency and complementarity across views, increasing the uncertainty of representation learning. Secondly, missing multi-label data compromise the modeling of label cor- relations and the completeness of supervisory signals. When both types of missingness occur at the same time, methods designed to handle only one type of missingness often fail to be effective (Tan) et al. 2018
In response to this challenge, systematic research on the problem of Incomplete Multi-View Multi- Label Classification (IMVMLC) has significant practical and theoretical value. This study mainly focuses on two existing technical directions. The first is multi-view consistency representation learn- ing. Representative works include DICNet (Liu et al. 2023b), which is based contrastive learn- ing and enforces representation consistency by constructing positive pairs across different views, and SIP (Liu et al.) 2024c), which follows the information bottleneck principle maximize shared information by preserving effective features while minimizing non-shared information. The second direction is multi-view fusion strategies, which include early fusion, intermediate fusion, and late fusion. Various representative methods explore different fusion paradigms. For example, AIMNet (Liu et al.) 2024a) adopts average fusion to obtain robust but relatively "smoothed" predictions. LMVCAT (Liu et al. 2023c) introduces learnable weights to adaptively allocate the contribution of each view feature, thereby improving discriminability. RANK (Liu et al. (025) employs view- quality-aware subnetwork to explicitly leverage multi-view complementarity, enabling the classifi- cation network to learn reliable cross-view fused representations.
However, these methods face certain limitations. In learning multi-view consistency representations, they often rely on loss-based constraints (e.g., contrastive learning) or regularization techniques that minimize non-shared information across views. When views are missing, such strategies easily lead to under-repressntatioo or wer-regularization, which limit the generalization ability of the model. Moreover, most existing fusion strategies overlook the structural information implied by label cor- relations, and many seanaabl--eight-baasee or duce additional training costs.
based fusion approaches intro-
To address these issues, we propose a method, Incomplete Multi- Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation (SCSD), as shown in Figure D First, for con- sistency representation, we introduce a shared codebook and cross-view reconstruction mechanism. The shared discrete codebook captures cross-view common semanties, while cross-view reconstruc- tion further enhances the consistency of the discrete representations. The limited nulli-view shared codebook embeddings reduce feature redundancy and enhance the generalization ability of the rep- resentations. Second, for decision fusion, we design This strategy assigns different weights to each view by estimating the ability of each view predic- tion to preserve the original label correlation structure, thereby reducing the impact of low-quality views. Finally, for the training paradigm, we adopt fused-teacher self-distillation: the fused predic- tion serves as the teacher signal guide the learning of each view-specific classifier. In this way, the global knowledge integrated across views is fed back into the single-view branches, improving con- sistency, robustness, and generalization during both training and inference. The main contributions of this paper are summarized as follows:
aneel-oorrelatoonorrenttee fusion strategy.
We propose a novel framework for incomplete multi-view multi-label classification based on a shared codebook and fused-teacher self-distillation. The framework handles arbitrary missing scenarios and achieves leading performance on multiple datasets, surpassing many advanced methods.
We propose to leam discrete consistent representations through multi-view shared code- book, which quantizes continuous features into a limited set of codebook embeddings. This design produces more compact representations and effectively reduces redundant informa-
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codebook
20
F43
Xa)
Encoder
Decouder
2
2020
x (2)
Uncoder
Decoder
[2 tt
Iudex DE Sequence
7(2)
970
fyee
576
x(m)
Encoder
Decondrr
(2 Ape
251
2
7(o)
iisssing supersiiorry signal
chaskk
Straight
gradient
Classifier
Classifien
P
Classffie
P
SO
S
The main framework of SCSD. The upper part represents the framework of multi-view consistent discrete representation learning, while the lower part represents the framework of multi- view prediction fusion and self-distillation.
Figure I:
tion. At the time, the features of different views naturally align in this shared codebook embedding space, which enhances the consistency of multi-view representations.
We propose weighted fusion method that assigns weights according to each view's ability to preserve label correlation structures its predictions. This method does rely on ad- ditional external networks or learnable weights and fully exploits the structural information inherent the supervision signals.
We introduce a fused-teacher self-distillation framework for multi-view predictions, in which the knowledge of all views fed back to each view branch through self-distillation loss, thereby improving the generalization ability of the model.
METHOD
2.1 PROBLEM DEFINITION
In this section, define the problem and introduce the notations. We consider multi-view dataset {X(e) 1 where denotes the number of views, and (v) E Raxd. with d,, representing the original feature dimension of the v-th view and n representing the number of samples. We define label matrix Y (0, 1) with categories, where Y,,, indicates that the i-th sample has the j-th label, and Yi = 0 indicates that the j-th label not assigned to the i-th sample. To handle missing views, introduce missing-view indicator matrix W E (0, 1] |mxm where Wi, = indicates that the j-th view of the i-th sample is observed, and Wi, = otherwise. Similarly, we introduce missing-label indicator matrix G {0, 10,1]xx, where Gi of sample i is observed and Gi = otherwise. Missing views and labels are filled with zeros. Our goal is to train model for multi-label classification under the condition where both views and labels are incomplete. In this paper. Xi,j. Xt., and X., denote the element, the i-th row, and the j-th column of matrix X. respectively.
means the j-th label
2.2 CONSISTENT DISCRETE REPRESENTATION LEARNING
In this section, we describe the process of learning multi view consistent discrete representations through shared codebook and cross-view reconstruction in three parts.
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Encoding. Since the original dimensionalities d, of different views in multi-view data are not identical, we first use view-specific MLP encoders to map the raw data into a unified dimensional space dg. Formally, {Z(e) E (X("))) -= where Z(0) E Raxdd denotes the continuous features of the v-th view, and E(e) denotes the MLP encoder of the v-th view.
Quantization. We subsequently discretize Z(0) through vector quantization (Van Den Oord et al. 2017 mapping each sample _ from view into a token sequence, i.c., a sequence of discrete codes. We first define a learnable shared codebook V = [ei]i= E TRdexde, which contains codes, each of dimensionality de- We adopt grouped quantization method (Baevski et al. 2019). which first splits ((d) into g segments. For clarity, taking the i-th sample from the v-th view as an example,
we obtain ( = [21,22, .2......... 2g IT E (d))s,))) where & E I& denotes the f-th feature segment and de = de/ g. We assign each 2₁ its nearest codebook embedding by nearest-neighbor lookup:
1" = arg min
3 (1(((z)) &2 (e,))l. j j= j......,k
Thus, we obtain the optimal quantization index t* for the (-th feature segment žt, and denote &, Err, where f2(-) represents & normalization used for codebook lookup (Yu al. 2021). Through this quantization operation, the original continuous feature 4, is mapped into an integer index sequence [1",2",. 2".. E {1....,k k}9, where each index t* corresponds to one codebook embedding. Finally, we retrieve the codebook embeddings according to these indices and concate- nate them to obtain the quantized discrete representation: _ = 2 2g E Idd, where [ denotes the concatenation operation. All other non-missing multi-view features z(() undergo the same quantization process to yield their discrete representations 2(())
Ext,
Reconstruction and Loss Function. For each view, we construct view-specific MLP decoder to reconstruct the original view X from its discrete representation 2()), denoted as {D(*) ) /==1- To better learn multi-vies view consistent representations, we introduce cross-view reconstruction: each view representation is decoded by different view decoders to reconstruct the original features, i.e.,
(X(s.e) D() (() "= 1,.... where X(,,)) denotes the reconstructed original features of view j from the representation view v. The reconstruction loss is defined as
ΣΣΣ
X (( xx Wa.j Wi.
Cree
E
ExE WA, W
We use an MSE-based reconstruction loss, where the missing-view indicator matrix W masks un- available views. The reconstruction loss is computed only when both view and view are avail- able, which reduces the influence of missing views on the model. Since the nearest-neighhor search in Eq iis non-differentiable, we follow (Van Den Oord al., 2017) and adopt a straight-throug! gradient estimator:
sg[2; & + 2,, where the gradient is directly copied from the decoder input to the encoder output. The codebook learning objective is defined
(1se((((z)] - -22(2)12 |1(2(z,) (+)()()))1
sample view
where sg[:] denotes the stop-gradient operation, i.e., sg[z] = and _. sg[z] = 0. The first term forces the codebook embeddings to be close to the encoder outputs, while the second term ensures that the encoder outputs are pulled toward a codebook embedding. We compute the loss over all non-missing samples: f.yg
Ei Wi, E E
Wi, (1.0)
In this part, our multi-view consistent discrete representation learning consists of m encoders, one quantizer, and m decoders. We quantize the continuous features ( into discrete represen- tations (() ym using the same shared codebook. Through shared codebook quantization, the features of different views are mapped into a limited set of codebook embeddings, which not only reduces redundancy but also allows common information across views be expressed consistently in the discrete space. Moreover, our cross-view reconstruction loss further enhances the learning of consistent multi-vien representations, reducing the need for additional alignment losses.
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2.3 CLASSIFICATION AND MULTI- VIEW DECISION FUSION
In this section, we introduce how to perform multi-label classification based on the view-consistent
discrete representations { v=1 learned in Section 2.2
Classification. We first construct a multi-label classifier F-) (-) for each view, which consists of
fully connected layer that maps (0) into the label space. Formally, { (F E
Raxe]m Jo=1 where (-) denotes the sigmoid activation function.
Fusion. Existing approaches for multi-view feature fusion and decision-level fusion mainly include average fusion, learnable weight fusion, uncertainty-awarc fusion, and muality-disrriminatoo fusion. Here, we propose to guide the evaluation of view prediction quality using label correlations, and then assign quantitative weights to each view prediction. Our method is more suitable for multi- view prediction fusion, as it fully exploits both multi-label supervision signals and label correlations.
Specifically, we first compute a label correlation matrix using the conditional probability matrix, following the approach in (Hang & Zhang 2021 Chen et al. 2019). The formulation is given as
Ex=1 Yr S. Y. +
Y.
Y.
Here, Si,y denotes the probability of label j occurring when label i occurs, é denotes a small scalar. The label matrix Y taken from the training set, and the final label correlation matrix is obtained
as S E Roxe_ Next, we compute the label correlation matrix for each view prediction p(()) =
W, p(t) in the same way:
E po) p(v) Pu r2 D D(U) p(0)
( p(O)
$10
(5)
+E (P!()) p(v) +
where nip denotes the batch size at the h-th training step. Through this formulation, we obtain J==
the label correlation matrices for each view, {m Rexe, which are computed using the predictions from the available views in the current batch. We then measure the ability of the v- th view to preserve label correlation structures by computing the Frobenius norm between s(r) and S, which serves as an indicator of prediction quality. Before computing the difference, we symmetrize and now-normalize both matrices obtain ((v) and S. The prediction quality score and view weights are defined as
exp(g(") /T) Ws. E exp (q(1)) /T) W...
(0)) = -115(0) - S11FF w
(6)
where the second term denotes the softmax normalization with temperature parameter т. This yields the weights of all views, {wo (2) pmm i 1,.., n. This method not only relies on the predic- tions of individual views but also explicitly leverages the global label correlation structure S. As result, the weight assignment prioritizes views that align with the global label dependency patterns and reduces the influence of noisy views on the fusion results. In each batch, S(₂) is updated ac- cording the current predictions, so the weights adaptively reflect the relative quality of different views across training stages and batches, rather than remaining fixed.
P
p(O)
Finally, the fused prediction P E Raxe is obtained by weighted fusion. We align the fused predic- tion P with the ground-truth labels through the binary cross-entropy loss:
n
C. = Ciee (P,
PPo)) Gr.s (8)
E
==
where the missing-label indicator matrix masks the effect of missing labels the model.
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Table 1: The summary statistics of different datasets are presented, where denotes the number of classes, c/n denotes the average number of positive labels per sample, denotes the number of samples, and m denotes the number of views.
Dataset
c/n
m
Corel5k
260
3.396
4999
6
Pascal07
20
1.465
9963
6
Espgame
268
4.686
20770
6
laprte 12
291
5.719
19627
6
Mirflickr
38
4.716
25000
6
2.4 SELF-DISTILLATION -BASED PREDICTION ENHANCEMENT
After obtaining the fused prediction P. further enhance the predictive ability of the model through a self-distillation framework (Zhang et al. 2021). Specifically, we use the multi-view fused prediction P as the teacher and the prediction of each individual view p(r) as the student, where the teacher prediction guides the learning of each student. The olff-istilllation loss is defined as:
Cdis
ΣΣ v=
ADKu(sg[Pu.l11PP + (1 X) (oc K.))/w. (9)
2i=1 2 W..
where A E [0, denotes the imitation parameter, sgl-] is the stop-gradient operation defined Sec- tion 2.2 Dx denotes the Kullback-Leible (KL) divergence, and Lace is the supervision loss for each view prediction p("), similar to Eq 8 Traditional distillation minimizes the KL divergence between teacher and student probabilities, assuming class probabilities sum one. This assump- tion fails in multi-label learning. To address this, we adopt the multi-label logit distillation (MLD) loss (Yang et al. 2023), which follows a one-versus-all strategy by decomposing the task into bi- nary problems and minimizing teacher-student probability differences for each, enabling effective distillation in multi-label learning.
This self-distillation framework uses the multi-view fused prediction as the teacher, which aggre- gates information from all views and provides a comprehensive and reliable supervisory signal. Each view-specific classifier serves student and learns from the teacher output, enabling it to capture the global knowledge contained in the fused prediction while preserving its own view-specific char- acteristics. As result, the framework improves consistency, robustness, and generalization during both training and inference.
2.5 OVERALL Loss FUNCTION
Finally, combine Eq model:
Eq
and Eq t to obtain the overall optimization objective of the Eq
L= L. + Lais + aLrrec + Logs
(10)
where is trade-off coefficient that balances the influence of different optimization objectives. This overall objective function jointly contributes to the optimization process from the perspectives of prediction accuracy, fusion self oiff-disiillation, reconstruction quality, and representation quantiza- tion.
Complexity Analysis. We first define dmax the maximum number of neurons in the intermediate layers of the network. The computational complexities of the four loss functions L.s Laiss Cree and fog O(nc), (nmc), O(nm²) and (nmg), respectively. The encoding -ecoodng stage has time complexity of (nm=²d,aa)) ), and the quantization process has time complexity of O (wwngk). The overall time complexity is thus O(nm² 2  + nmgk + nc nmc+ nm2 + nmg)). In summary, the overall computational cost of SCSD is dominated by the multi-view encoding decoding process. The overall complexity grows linearly with the sample size n, and the framework exhibits good scalability in multi-view scenarios.

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Published as a conference paper at ICLR 2026
The results under the setting of 50% missing views, 50% missing labels, and 70% training data are reported. The table lists mean and standard deviation (bottom-right). Ave.R denotes the average rank across metrics. Bold numbers indicate the best results, and underlined numbers indicate the second-best.
Table 2:
Dataset Metric
iMyWI.
NATMAL
DDINet
DICNet
MTD
RANK
DRLS
SCSD
Sources
18
2PAMT'22
TNNLS'
AAAT23
NewrIPS223
ICML:24
25
CVPR'25
AP
0.283g.00s oos
0.3096.004
0.360p.009
0.3780.004
0.4133.007
0.4160.015
0.4266..00
0,433 oos
0.4470.010
1-IIL
0.978a.00n
0.987o.
0.98Ig .o0
0.98I,
000
0.988
.000
0.988.. 000 0.988p.000
0.988a..00
0.988o.080
Coret
I-RL.
0.865g oos
0.8780.002
0.865p.co cos
0.877o
0.892p 004
0.910
003 0.913c
0.916
0.920p.002
AUC
0.868g oos
0.881o.002
0.868a.com
0.881o.00s
0.895p.o0r
0.912o. oos 0.915p.o0s
0.918,
002
0.923b.003
1-OB
0.311a.03 0.350p.009
0.437a.012
0.4644.012
0.491p.o10
0.4922..03
0. 091
4.5099.019
0.526p.01s
I-Cow
0.
0.702a.00e o0s
0.7250.on6
0.689
0.7140.010
0.748g
0.786o.
000
0.798c.006
0.8044
oos
0.811o.on6
Ave.R
8.500
6.667
7.500
6,333
4.167
3.333
3,000
1833
1.000
AP
0.437a.01s
0.4886.ons
0.532p.c10
00002
0.050p.004
0.550%.
00e
0.5540.009
0.567, oos
0.578o.on9
1-I1L
0.882, oos
0.928%.001
0.932p.co
.01
0.930 001
0.932p.oo 0011
0.931e 00a
0.932o.001
0.934..001 001
0.934o.001
Procal07
I-RL.
0.7366.033 015 0.7836.001
0.808 0og
0.781
007
0.830g.o0 000
0.825 0od
0.826c.004
0.843
0.8466.006
AUC
0.767a.015 0.8116.00:
0.829g.004
0,805g.oo
oos
0.819p.o0s
0.8455..0x
0.818p.oos
0.864, 003
0.866..004
1-OB
0.3622.023 023 0.421o.0m6
0.448g.015
0.4266..0 013
0.457
0.4663.012
0. 0.165p.015
0.477
0.189p.011
I-Cw
0.677a.016 0.727o.002
0.757
0.728%.00
0.783p
.04
0.777o.
000 0.779g.on6
oos 0.798,
0.801o.ons
Ave.R
8,833
7,500
5,333
T.167
3.500
4.833
3,500
1,833
1.000
AP
0.244a.00s
0.246o.on2
0.286g.004
0.2999. 004
0.306p 000
0.310o.
004 0.3140.004
0.326, oo5
0.345o.004
1-IIL
0.972 non
0.983o.000
0.983 coo
0.983. 000
0.983, ooe 0.983 0o0 0.983..000
0.983..000
000
0.983o.000
Espgame
I-RL.
0.808g.002 oo2 0.818
0.815p.co0 co0
0.833 003
0.837p.001 001
0.849 002 0.849p.002
0.856, 002
0.863o.002
AUC
0.8133.00 o02 0.824o.002
0.819p.o0m
0.837o.002
0.842p.00:
0.8536.002
0.853p.o02
0.862, 0o2
0.867o.on2
1-OE
0.3433. oag 0.3396.ons
0.427a.010 0.00
0.437o.010
0.448p.006
0.451o.012
0.460o..10
0.473 0o1
0.4916.010
I-Cow
0.548g 004 0.57 lo.os
0.553p.con
.ooo
0.598p.a06
0.601
0.628%.
004
0.632p.o06
0.652,
pog
0.6570.o84
Ave.R
8,833
6.500
6.500
5.167
4.333
3.167
2.667
1 1883
1.000
AP
0.237a oos 0.261o.001
0.302a.co
cos
0.327o.
0o0
0.332p
.002
0.331o.
007 0.3470.004
0.356, 0000
0.385o.ons
1-IIL
0.969 ooo 0.980,.000
g.980p.com
0.980,.00
000
0.981
000
0.981.
000 0.981c.000
0.981.
000
0.981o.000
1aprte12
I-RL
0.8330 oog 0.8486.001
0.853a.c 002
0,872o 002
0,875B 001
0.887o
004 0.888c.002
0.896, 003
0.903o.002
AUC
0. 0.835g.001
0.850p.00:
0.855p.oon
0.8730.001
0.876p.oo
0.888..003
0.889p.002
0.898, 0o2
0.905..002
I-OE
0.352g.00 oos
0.300o.ons
0.4
0.471p.o06
0.4666..01
001
0.4866o.12
0.490
0.514b.o0s
I-Co
0.5644.005
0.502p.004
0.594p.co7
0.648o.a05
0.649p.ooz
0.679%.oos
0os
0.6866.o06
0.707, 007
0.721 0.721o.ons
Ave.R
9.000
7.667
6.833
6,000
4.000
3.833
2.667
1883
1.000
AP
0.490g.012
0.55lo.002
0.588p.c0a
0.5866.005
0.608p.004
0.615o.
004
0.606g.006
0.630, 0.000
0.634o.on5
1-IIL
0.839 ooz
0.8826.00:
0.888
0.8886.0
0.891, 001
0.891,
001
0.891
0.895..001
0.895p.001
Mirllekkr
I-RL
0.803o oos
0.8440.001
0.865p.c
0,861o.00
0.875p 000
0.878
002
0.8746.002
0.885, 002
0.888o.002
AUC
0. 0.787a.012 0.8370.00
0.853
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3 EXPERIMENTS
3.1 DATASETS AND METRICS
Datasets. We follow the experimental settings in several IMVMLC studies to comprehensively evaluate the performance of the proposed model (Liu et al. 2024b Yan et al. 2025). We conduct experiments on five multi-view multi-label datasets, namely CorelSk (Duygulu et al. 2002). Pas- cal07 (Everingham et al. 2010). Espgame (Von Ahn & Dabbish 2004). laprte 12 (Grubinger et al.)
2006). and Mirflickr (Huiskes Lew| 2008). More details about these datasets are provided in Table We use six different types of features from these datasets as six views: DenseSift (1000), DenseHue (100), GIST (512), RGB (4096), LAB (4096), and HSV (4096), where the number in parentheses denotes the feature dimensionality.
Evaluation Metries. Following previous work (Liu et al. 2023b 2024c), we evaluate our model and all baseline methods using six commonly used metrics for lubti-label classification. These include Average Precision (AP), Hamming Loss (HL), Adapted Area Under Curve (AUC), Ranking Loss (RL), OncError (OE), and Coverage (Cov). For four of these metries, we record 1 HL,, 1 1-RL, 1-OE, and 1 Cov in figures and tables. In this way, all six evaluation metrics follow a consistent convention: larger value indicates better performance.
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Figure 2: The radar charts are based on results with complete views, complete labels, and 70% training data, covering nine methods, five datasets, and six metrics. In each chart, the center denotes the worst result and the vertex denotes the best.
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Figure 3: The parameter sensitivity analysis of the SCSD model is conducted under the setting of 50% missing views, 50% missing labels, and 70% training data.
3.2 COMPARISON METHODS
To more comprehensively evaluate the effectiveness of the proposed method, we select eight incom- plete multi-view multi-label learning methods specifically designed for the dual-missing problem as baselines in the comparative experiments. This enables more comprehensive evaluation of the model's 's ability to handle dual- maiaiimsssingg scenarios. The specific methods include iMvWL (Tan al.] 2018), NAIM3L (Li & Chen 2021). DDINet (Wen et al. 2023), DICNet (Liu et al. 2023b) MTD
(Liu et al. 2024b), SIP (Liu et al. 2024c), RANK (Liu et al. 2025), and DRLS Yan et 2025). whose related descriptions are already provided in the Introduction I and Related Work A. sections.
3.3 IMPLEMENTATION DETAILS
To simulate the random missingness of multi-viers and multi-label data in real-world scenarios, we follow previous studies to generate missing data (TTn et al. 2018 Liu et al. 2024c). Specifically, for
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multi-view data, randomly discard 50% of the views while ensuring that each sample retains at least one available view. For multi-label data, we randomly discard 50% of the positive and negative labels, and we use zeros to fill in the missing views and labels. The dataset is divided into 70% for training and 30% for validation and testing. The proposed SCSD model is implemented in PyTorcc and the experiments
conducted on an Ubuntu operating system with RTX 4090 GPU and an 19-13900K CPU. The learning rate is set to 0,001, the optimizer is AdamW with a weight decay of 0.001, and the batch size is 128. The codebook is initialized with k-means, the codebook size is set to 2048, and the codebook embedding dimension de is set to 4.
3.4 EXPERIMENTAL RESULTS
Table 2 compares eight state-of-the-ane methods on five public multi-view multi-label datasets, with both view and label missing rates 50%. It can be observed that the proposed SCSD model out- performs all baseline methods, especially on the AP metric of the Espgame and Iaprtc 12 datasets, where SCSD achieves improvements of 5.83% and 8.15% over the second- bestt method, DRLS. The Espgame and laprte 12 datasets have more complex label spaces. which introduce a higher level of learning difficulty. Achieving significant improvements under these more challenging label struc- tures indicates that SCSD has stronger capability to model complex multi-label relationships and learn cross- cross-view consistency. Compared with DICNet, which learns multi-view consistent features through contrastive loss, and SIP, which suppresses non-shared information based on the informa- tion bottleneck principle to obtain consistent representations, the proposed SCSD achieves average improvements of 14.94% and 8.65% in AP across the five datasets. These results clearly demon- strate the advantage of SCSD in multi-view consistent representation learning. This comparative experiment thoroughly validates the effectiveness of SCSD for multi-label classification under the dual-missing scenario.
In addition, we also conduct comparative experiments under the setting of complete views and com- plete labels, as shown in Figure 2 It can be observed that SCSD achieves the best performance on most metries across five datasets, which strongly demonstrates the generality of SCSD. Under the condition where both views and labels are complete, SCSD still achieves the best or near-best performance on most metrics. This suggests that the consistency representation learning mecha- nism built on the multi-view shared codebook has strong inherent representational capacity, and its effectiveness is not limited to cases with missing information.
3.5 PARAMETER ANALYSIS
Our model contains three hyperparameters: a in Cree- X in Lais. and the softmax temperature parameter in decision fusion. Figure 3 presents the parameter sensitivity results of the SCSD model. Figures Baj and 33 show the AP metric of SCSD on Corel5k and Pascal07 under different combinations of a and X. We observe that on the Corel5k dataset, SCSD exhibits performance fluctuations when a = le performance of SCSD remains relatively stable. On Corel5k, the best results obtained when takes values in the range [le - 2, 1e0) and À takes values the range [le 2, 2e - 1]. whereas on Pasca107, better performance is achieved when a takes values in the range [5e0, 2e1] and À takes values in the range [le- - 2, 2e - 1]. Figures [eguresBB]and]] 3d present the influence of t on the model, where the left y-axis indicates AP and the right y-axis indicates AUC. The proposed method is not sensitive to variations of the temperature parameter r. On Corel5k, 7 takes values in the range (5e - 1, 5e0] to achieve the best results, while on Pasca107, takes values in the range [1e 1, 5e 1] for the best performance.
or a 2e1, which are extreme values, while on Pasca107 the
3.6 ABLATION STUDY
Table presents the ablation study of SCSD, where the gray background in the middle highlights the full version of SCSD. The upper part removes different loss functions. Among them, Lais denotes the first term in Lais. which encourages the student to imitate the output of the fused teacher. We observe that removing any loss function leads performance drop of SCSD. The lower part of the table removes certain structural designs. In the fifth row, "w/o VQ" indicates that vector quantization is not used, and the continuous features {m)), output by the encoder are directly employed. A clear performance drop is observed, since multi-view shared codebook design

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Table 3: The ablation results on two datasets under the setting of 50% missing views, 50% missing labels, and 70% training data are reported. Here, "w/o" denotes "without". The bold numbers indicate the best results, while the underlined numbers indicate the second-best results.
Corel5k
Pasca107
Method
AP
I-RL
AUC
AP
I-RL
AUC
SCSD w/o Ldis
0.376
0.882
0.884
0.560
0.834
0.855
SCSD w/o LdisKKL
0.411
0.906
0.909
0.572
0.843
0.864
SCSD w/o Lree
0.439
0.916
0.919
0.560
0.839
0.860
SCSD
0.447
0.920
0.923
0.578
0.846
0.866
SCSD w/o VQ
0.430
0.914
0.916
0.565
0.841
0.860
SCSD w/o cross view rec
0.442
0.918
0.921
0.553
0.837
0.859
SCSD w/o S fusion
0.445
0.919
0.922
0.570
0.844
0.864
better supports consistent representation learning. In the sixth row, "wlo cross_view view_r"eewrree" denotes removing cross-view reconstruction and training with standard single-view reconstruction, which also results in performance degradation some extent. The last row, "w/o S fusion," denotes moving our weighted fusion strategy and replacing it with a simple masked average fusion strategy:
v=1 P. W(,)// Ev= W.... where observe a performance decline, especially on the Pasca107 dataset. This is because Pascal07 has 20 labels, which provide a more reliable label correla- tion matrix S, enabling our fusion strategy to better identify the quality of predictions from different views. Overall, we find that the contributions of the multi-view shared codebook and self-distillation are the most significant for the performance of SCSD.
P..: (E m
4 CONCLUSION
In this paper, we propose a novel method for incomplete multi-view multi-label classification. First, we use a multi-view shared codebook to learn consistent discrete representations across views, and we further enhance the consistency of different view representations through a cross-view recon- struction mechanism. Then, allocate different weights by evaluating the ability of each view prediction to preserve label correlation structures, and we perform weighted fusion to obtain the fused prediction. Finally, view prediction, and we feed the knowledge of all views back into each view-specific branch through the self-distillation loss, thereby improving the generalization ability the model. Extensive exper- iments demonstrate that the SCSD method effectively addresses the problem of multi-view multi- label classification under dual-misst muil-missing conditions.
the fused prediction as the teacher to guide the learning of each
Limitations. Although our method achieves strong performance on the incomplete multi-view multi-label learning task, it still has several limitations that may affect its applicability in broader scenarios. First, introducing a multi-view shared codebook brings additional memory and compu- tational overhead. The memory overhead mainly comes from storing and updating the codebook embeddings, while the computational cost is largely due to computing the distance matrix between input features and the codebook embeddings during quantization. In addition, the quantization mod- ules in SCSD assume that representations from different views can be aligned in a shared latent space. When the view-missing rate becomes very high, the amount cross-view information avail- able for alignment is greatly reduced, which can weaken the generalization ability of the shared codebook mechanism.
REPRODUCIBILITY STATEMENT
All experiments this paper are conducted on five publicly available multi-view multi-label datasets, ensuring that no private proprietary data are used. The pseudocode of the training procedure is provided in Appendix A.2 The source code of SCSD is available on GitHub.
ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China (Grant No. 62476166 and No. 62576206).
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A
APPENDIX
A.1 RELATED WORK
Multi- View Multi-Label Learning. Under complete views and labels, several representative meth- ods have been proposed. SIMM (Wu et al., 2019) jointly optimizes a confusion aadeesarraa loss and multi-label loss to exploit shared information, while imposing orthogonal constraints on the shared subspace to preserve discriminative features. To explicitly address both view consistency and diver- sity, CDMM (Zhao et al.) 2021) models view consistency with independent classifiers, incorporates the Subbert-Schmidt independence criterion to capture diversity, and introduces label correlations and view contribution factors enhance performance. From a graph-based perspective, D-VSM (Lyu et al. 2022) encodes view features with deep GCNs and integrates cross-view relations within unified graph. Moreover, focusing on label dependency modeling, ELSMML (Liu al.] (2023a) constructs a label correlation matrix using high-order strategies, combines dimensionality reduction to extract latent semantic features, introduces manifold regularization to preserve structural infor- mation, and trains classifiers with an accelerated optimization algorithm.
Incomplete Multi-Vier Multi-Label Learning. To handle missing views and labels, several meth- ods have been developed for incomplete multi-view multi-label learning. iMvWL (Tan et al. 2018) learns cross-view relationships and weak label information simultaneously the shared subspace,
while capturing local label correlations and learning the corresponding predictors. To tackle label insufficiency and view misalignment under incomplete settings, NAIM3L (Li & Chen 2021) alle- viates label insufficiency through consistency constraints and label structure modeling, and jointly models both global and local structures in a common label space. From a network architecture perspective, DDINet (Wen al. 2023) consists of feature extraction, weighted fusion, classifica- tion, and decoding modules, effectively integrating available data and labels under dual-missing scenarios. By introducing masked mechanism, MTD (Liu al. 2024b) proposes masked dual- channel disentanglement framework that separates representations into shared and private channels, and enhances feature learning with contrastive loss and graph regularization. Moreover, focusing on representation disentanglement, DRLS (Yan et al. (2225) extracts shared features via cross-view re- construction, learns view-specific features with mutual information constraints, and leverages label correlations to guide semantic embeddings for preserving topological structures.
Algorithm 1:
The training process of SCSD
Input: Incomplete multi-view data (X())) / incomplete label matrix Y, missing-vien indicator matrix W, missing- label indicator matrix G, hyperparameters a, X, and T, and training epochs H.
Output: Prediction P.
Initialize the model parameters. Use Eq to compute the label correlation matrix S. Set = False.
bodebook_inittalfe 2 for h = 1 to do
Extract multi-view continuous features (2(x) E(e) ((())) through the encoders.
3
4 Split the non-missing features (2(²) ) into feature segments
(Z [1, 22, 22,........................ ...; Zg] IT E R 9x(.//))
......... n, l...., m, W.o # O).
5
if not coddcook.iiiiiaalize..... initialized then
Use all available view features (2((0)) within the current batch to perform k-means clustering for initializing the codebook embeddings.
7
codebook inntoiaiieitttteeed = True
8
Use Eq to find the nearest codebook embedding et- for each 2,, and concatenate them to obtain the discrete features (2 -
9
Obtain the cross- view reconstruction results through the decoders:
(x = (2())
)m j 1,..,
Obtain the predictions of each view through the classifiers: {P(r) o (F (2()) als Compute the weights according to Eq 5 7 and obtain the fused multi-view prediction P.
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12
Compute the overall loss L according to Eq and update the parameters.
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Figure 4: The experimental results of the SCSD model under different view-missing rates, different label-nissing rates, and different training set proportions are reported. The figure presents two datasets and three evaluation metrics.
A.2 ALGORITHM
The training procedure of the SCSD model is provided in Algorithm
A.3
ADDITIONAL EXPERIMENTAL RESULTS
Missing and Training Sample Rates Analysis. Figures 4a and 4b show the results of the SCSD model under different view-mis hiew-missing rates when the label-missing rate fixed at 50%. Figures 4c and present the results under different label-missing rates when the view-missing rate is fixed at 50%. As the view-missing rate or the label-missing rate gradually increases, the model performance also decreases. However, our model is able maintain relatively stable performance even when the missing rate reaches 70%. Moreover, we observe that increasing the view-missing rate has greater impact on our model than increasing the label-min sabel-missing rate. This is because our model relies on the learned multi-view consistent representations, and the quality of the learned representations decreases when the view-missing rate increases. Figures 4e and 4f show the results of SCSD under 50% missing views and 50% missing labels with different proportions of the training set. As the proportion the training set increases, the model performance also improves. Furthermore, our model achieves satisfactory result even under the extreme case of only 10% training data.
Additional Parameter Analysis. In Figures aaland Fag 5bb] we also retrain and evaluate the model with different scales of the codebook size l and the codebook embedding dimension de to systematically analyze the impact of the multi-view shared codebook on representation capacity and final perfor- mance. The experimental results show that appropriately increasing the codebook size improves the model performance within a certain range, but overly large values lead to higher computational cost with diminishing returns. the same time, a smaller embedding dimension stabilizes the quantiza- tion process and improves codebook utilization, thereby leading to better model performance.
Codebook Utilization Analysis. Figure 6 s shows the changes in codebook utilization of the SCSD model on the validation set during the training process. In this codebook utilization experiment, we compute the utilization rate by counting the number of codebook embeddings that are actually selected during the forward pass and dividing it by the total codebook size; codebook embeddings that are not assigned to any input features are regarded as inactive. This approach intuitively reflects how well the model covers the codebook prototypes during the quantization stage and indicates the efficiency the model. We only present 10 epochs, because afterward all datasets maintain 100% codebook utilization until the end of training. From the figure, we observe that SCSD reaches 100% codebook utilization within only a few epochs on all datasets and keeps it stable throughout the subsequent training. This indicates that SCSD is able to fully activate all embedding units in the shared codebook, thereby avoiding the codebook collapse problem (i.c., only a very small number of codebook embeddings frequently used while most vectors remain idle and inactive,
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Figure 5: An additional parameter sensitivity analysis of the SCSD model is conducted under the setting of 50% missing views, 50% missing labels, and 70% training data.
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Figure 6: The codebook utilization of the SCSD method is reported under the training setting of 50% missing views, 50% missing labels, and 70% training data, covering all five datasets.
leading to insufficient representation capacity and low information utilization). In other words, the shared codebook design of SCSD not only preserves the rich representational capacity of multi- view data but also effectively suppresses redundant features through a limited number of codebook embeddings, thereby enhancing the generalization ability of the learned representations.
A.4 LARGE LANGUAGE MODEL USAGE STATEMENT
this paper, we use large language model to polish the introduction section.
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Reviews:
[{"review_id": "0i50ggTXLW", "reviewer": "Reviewer_Jx7D", "summary": "This paper addresses the dual-missing problem in multi-view multi-label learning by introducing the SCSD model. The proposed framework leverages a shared codebook to learn consistent representations across views, employs an adaptive weighted fusion mechanism to aggregate multi-view predictions based on their reliability, and adopts a self-distillation strategy to enhance model generalization. Experimental results on five benchmark datasets consistently demonstrate that SCSD outperforms existing state-of-the-art methods under various incomplete-view settings.", "questions": "Refer to Weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe proposed weighted fusion strategy, grounded in the label correlation structure, effectively assesses the contribution of each view without introducing extra parameters, thus maintaining model efficiency.\n2.\tThe model exhibits robust and stable performance under different levels of missing data, indicating strong adaptability to scenarios with both missing views and missing labels.\n3.\tThe paper is clearly presented and well-structured, with coherent notation and logical flow, making it accessible and easy to follow.", "weaknesses": "1.\tThe parameter sensitivity analysis lacks a systematic evaluation of how varying the codebook size and embedding dimension affect model performance—two crucial factors that directly influence representational capacity.\n2.\tThe problem formulation in Section 2.1 is somewhat lengthy, and the accompanying notations and descriptions could be further streamlined to improve clarity and readability.\n3.\tThe hyperparameter tuning ranges for $\\alpha$ and $\\lambda$ are provided ($[1e^{-2}, 2e^{1}]$ and $[1e^{-3}, 5e^{-1}]$, respectively), but the rationale behind these specific search intervals is not discussed.\n4.\tIt remains unclear whether the shared codebook mechanism introduces constraints related to the number of views or the feature dimensionality, and a discussion on this point would strengthen the paper’s technical completeness.", "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": "7AmGnsMPsj", "reviewer": "Reviewer_3pou", "summary": "This paper proposes the SCSD framework, which aims to learn consistent multi-view representations under missing-view scenarios. The method enforces representation consistency through a shared cross-view codebook, strengthens semantic alignment via cross-view reconstruction, and adaptively refines view-specific weights based on label correlations. By integrating these components into a unified architecture, the proposed approach achieves robust and reliable performance across various benchmark datasets, demonstrating both novelty and promising experimental potential.", "questions": "Please refer to the Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe study addresses the dual-missing problem in multi-view multi-label learning, a challenging and practically significant research direction that has received limited prior exploration.\n2.\tThe proposed shared codebook mechanism effectively mitigates instability in semantic consistency learning under missing-view conditions, outperforming existing methods theoretically and empirically.\n3.\tThe experimental design is rigorous and systematic, and the ablation studies clearly validate the contribution of each component within the SCSD framework.", "weaknesses": "1.\tAlthough Table 2 presents extensive quantitative results, the corresponding analysis in Section 3.4 is relatively concise. A more detailed discussion from multiple perspectives (e.g., robustness, scalability, or dataset characteristics) would improve the interpretability of the findings.\n2.\tThe overall loss function in the method section comprises several components. Adding a concise explanatory paragraph summarizing their roles would help readers grasp the optimization objective more intuitively.\n3.\tWhile the use of a shared codebook to learn discrete representations is effective, the paper offers insufficient intuitive explanation or conceptual justification for why discretization enhances performance. Providing such insight would strengthen the theoretical grounding of the approach.\n4.\tGiven the potential heterogeneity of feature distributions across different views, it would be valuable to discuss whether the shared codebook design might unduly restrict view-specific representations, potentially leading to semantic compression or information loss.", "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": "1E3QjzhvHU", "reviewer": "Reviewer_Z7Gz", "summary": "This paper addresses the challenge that existing incomplete multi-view multi-label classification methods fail to effectively capture stable and discriminative shared semantic representations. To overcome this limitation, the authors propose a novel framework named Incomplete Multi-View Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation (SCSD). The SCSD model learns discrete and consistent representations across multiple views through a shared codebook and a cross-view reconstruction mechanism. Moreover, a view-weight estimation strategy is introduced to adaptively assess the relative importance of different views, thereby improving the quality of the fused prediction. In addition, a fused-teacher self-distillation framework is designed to enhance the generalization capability of the model. Extensive experiments conducted on multiple benchmark datasets demonstrate the superior performance and effectiveness of SCSD compared with existing state-of-the-art methods.", "questions": "Refer to the weaknesses above.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe proposed approach effectively integrates a shared codebook and cross-view reconstruction to learn consistent discrete representations across multiple views. The method is conceptually well-founded and supported by solid experimental evidence.\n2.\tThe weight estimation strategy and a teacher self-distillation framework are introduced to improve the quality of view fusion and the generalization of the proposed model.\n3.\tComprehensive experiments on five publicly available benchmark datasets show that SCSD achieves superior performance across various evaluation metrics, confirming its robustness and effectiveness.", "weaknesses": "1.\tIn Figures 3(a) and 3(b) (page 8), the axis labels are too small, which negatively affects readability. Enlarging the font size would improve the presentation quality.\n2.\tIn the overall optimization objective (Eq. 10), four different loss terms are included, but a weighting coefficient is applied only to the third one. The reasoning behind this particular design choice should be further clarified.\n3.\tThe process for determining the size of the shared codebook is not clearly explained. It would be valuable to discuss whether increasing the codebook size consistently improves performance or whether the gains plateau beyond a certain point.\n4.\tThe manuscript would benefit from a more detailed discussion of the limitations of the proposed approach, which would make the evaluation of SCSD more balanced and comprehensive.", "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": "VakGFS0qQz", "reviewer": "Reviewer_gK1A", "summary": "This paper proposes a novel framework, SCSD, designed to address incomplete multi-view data scenarios that involve both missing views and missing labels. The approach incorporates three core components: (1) a shared codebook that facilitates the learning of discrete and consistent representations by aligning heterogeneous views within a compact latent space; (2) an adaptive view-weight estimation mechanism that leverages label correlations to enhance the reliability of fused predictions; and (3) a fusion-guided self-distillation strategy that transfers holistic knowledge from the fused predictions back to individual view branches, thereby improving generalization and strengthening inter-view collaboration.", "questions": "See the weaknesses above.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1.The SCSD framework enhances the learning process from three complementary aspects—representation learning, decision-level fusion, and training strategy—resulting in a well-organized and conceptually coherent design.\n2.The model exhibits strong scalability, and its discrete representation learning module can be easily adapted or extended to other tasks and architectures.\n3.The ablation studies clearly demonstrate that the fusion-teacher self-distillation mechanism substantially boosts classification accuracy, validating its effectiveness.", "weaknesses": "1.While the framework integrates several functional modules that could affect computational efficiency, the paper does not provide a time complexity or efficiency analysis. Including such an evaluation would offer readers a clearer picture of the model’s practical scalability.\n2.The codebook utilization analysis in the appendix lacks details on how the utilization rate is quantified, which reduces reproducibility.\n3.In the experimental section, the authors primarily report superior results over baselines but do not offer deeper performance interpretation or insight. A more thorough discussion would enhance the paper’s contribution.\n4.Since SCSD jointly addresses both missing views and missing labels, it would be helpful to discuss whether any trade-off exists between these two factors—specifically, whether the model exhibits higher sensitivity to one form of incompleteness than the other.", "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)