Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning
Authors: Mingcheng Li
Dingkang Yang
Yang Liu
Shunli Wang
Jiawei Chen
Shuaibing Wang
Jinjie Wei
Yue Jiang
Qingyao Xu
Xiaolu Hou
Mingyang Sun
Ziyun Qian
Dongliang Kou
Lihua Zhang
Venue: NeurIPS 2024 poster (2024)
Keywords: Human intention understanding
modality missing
Primary area: machine_learning_for_other_sciences_and_fields
TL;DR: We propose a hierarchical representation learning framework to handle the problem of uncertain missing modalities in the multimodal sentiment analysis task.

Abstract: Multimodal Sentiment Analysis (MSA) is an important research area that aims to understand and recognize human sentiment through multiple modalities. The complementary information provided by multimodal fusion promotes better sentiment analysis compared to utilizing only a single modality. Nevertheless, in real-world applications, many unavoidable factors may lead to situations of uncertain modality missing, thus hindering the effectiveness of multimodal modeling and degrading the model’s performance. To this end, we propose a Hierarchical Representation Learning Framework (HRLF) for the MSA task under uncertain missing modalities. Specifically, we propose a fine-grained representation factorization module that sufficiently extracts valuable sentiment information by factorizing modality into sentiment-relevant and modality-specific representations through crossmodal translation and sentiment semantic reconstruction. Moreover, a hierarchical mutual information maximization mechanism is introduced to incrementally maximize the mutual information between multi-scale representations to align and reconstruct the high-level semantics in the representations. Ultimately, we propose a hierarchical adversarial learning mechanism that further aligns and adapts the latent distribution of sentiment-relevant representations to produce robust joint multimodal representations. Comprehensive experiments on three datasets demonstrate that HRLF significantly improves MSA performance under uncertain modality missing cases.

Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning
Mingcheng Li Dingkang Yang 13+
Yang Liu' Yue Jiang 1.3 Qingyao Xu
Shunli Wang 1.3
Jiawei Chen Xiaolu Hou 1.3
Shuaibing Wang Jinjie Wei
Mingyang S.n Ziyun Qian 13
Dongliang Kou! 1.3
Lihua Zhang! 1,2,3,455
Academy for Engineering and Technology. Fudan University, Shanghai, China Institute Metaverse Intelligent Medicine, Fudan University, Shanghai, China Cognition and Intelligent Technology Laboratory, Shanghai, China
Jilin Provincial Key Laboratory of Intelligence Science and Engineering, Changchun. China Engineering Research Center of Al and Roboties, Ministry of Education, Shanghai, China. mingcheng1i21 1210m. fudan. edu. en, dkyang208fudan. edu.
Abstract
Multimodal Sentiment Analysis (MSA) is an important research area that aims understand and recognize human sentiment through multiple modalities. The com- plementary information provided by multimodal fusion promotes better sentiment analysis compared to utilizing only single modality. Nevertheless, in real-world applications, many unavoidable factors may lead to situations of uncertain modality missing, thus hindering the effectiveness of multimodal modeling and degrading the model's performance. To this end, we propose a Hierarchical Representation Learning Framework (HRLF) for the MSA task under uncertain missing modali- ties. Specifically, we propose a fine- sine-grained representation factorization module that sufficiently extracts valuable sentiment information by factorizing modality into sentiment-relevan and modality specific representations through crossmodal translation and sentiment semantic reconstruction. Moreover, hierarchical mutual information maximization mechanism is introduced to incrementally maximize the mutual information between multi-scale representations to align and reconstruct the high-level semantics in the representations. Ultimately, we propose a hierarchical adversarial learning mechanism that further aligns and adapts the latent distribution of sentiment-relevant representations to produce robust joint multimodal repre- sentations. Comprehensive experiments on three datasets demonstrate that HRLF significantly improves MSA performance under uncertain modality missing cases.
Introduction
Multimodal sentiment analysis (MSA) has attracted wide attention in recent years. Unlike unimodal emotion recognition tasks [9, 63, 64, 53, 56), MSA understands and recognizes human emotions through multiple modalities, including language, audio, and visual [31, 58]. Previous studies have shown that combining complementary information among different modalities facilitates valuable semantic generation [41, 40, 61, 55, 62]. MSA has been well studied far under the assumption that all modalities are available in the training and inference phases [12, 66. 54, 57, 56, 25, 59, 60J. Nevertheless, in real-world applications, modalities may be missing due to security concerns, background noises, sensor limitations and so on. Ultimately, these incomplete multimodal data significantly hinder the performance of MSA. For instance, as shown in Figure the entire visual
'Equal contributions. 'Corresponding authors.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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modality and some frame-level features in the language and audio modalities are missing, leading to an incorrect prediction.
In recent years, many studies [8, 28, 26, 49, 37, 50, 76, 74, 68, 27, 23, 22] attempt to address the problem of missing modalities in MSA. For example, SMILL [29] estimates the latent features of the missing modality data via Bayesian Meta-Learning. However, these methods are constrained by the following factors: (i) Implementing complex feature interactions for incomplete modalities leads to a large amount of information redundancy and cumulative errors, resulting in ineffective extraction of sentiment semantics. (ii) Lacking consideration of semantic and distributional alignment of representations, causing imprecise feature reconstruction and nonrobust joint representations.
To address the above issues, we propose Hi- erarchical Representation Learning Framework (HRLF) for the MSA task under uncertain miss- ing modalities. HRLF has three core contribu- tions: (i) We present a fine-grained representa- tion factorization module that sufficiently ex- tracts valuable sentiment information by fac- torizing modality into sentiment-relevant and modality-speciffe representations through intra- and moter-modality translations and sentiment se- mantic reconstruction. (ii) Furthermore, a hierar- chical mutual information maximization mech- anism is introduced incrementally align the high-level semantics by maximizing the mutual information of the multi-scale representations of both networks in knowledge distillation. (iii) Eventually, we propose a hierarchical adversar- ial learning mechanism to progressively align the latent distributions of representations leveraging multi-scale adversarial learning. Based on these components, HRLF significantly improves MSA performance under uncertain modality missing cases on three multimodal benchmarks.
Sample
Prediction
Label
Language
But for the looe just lille bored.
Audio
Negative
Visual
Negative
Language
But me wad titla litte frores
Audio
Positive
A case of incorrect prediction by the tra- ditional model with missing modalities. The pink and yellow areas indicate intra- and inter-modality missingness, respectively.
Figure 1:
Related Work
2.1 Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) seeks to comprehend and analyze human sentiment by utilizing diverse modalities. Unlike conventional single-modality sentiment recognition, MSA poses greater challenges owing to the intricate nature of processing and analyzing heterogeneous data across modalities. Mainstream studies in MSA [69, 70, 44, 12, 11, 42, 25) focus on designing complex fusion paradigms and interaction mechanisms to improve MSA performance. For instance, CubeMLP [42] employes three distinct multi-layer perceptron units for feature amalgamation along three axes. However, these methods rely on complete modalities and thus are impractical for real- world deployment. There are two primary approaches for addressing the missing modality problem in MSA: (1) Generative methods [8, 28, 26, 49] and (2) joint learning methods [37, 50, 76, 74, 68, 27]. Generative methods aim to regenerate missing features and semantics within modalities by leveraging the distributions of available modalities. For example, TFR-Net [67] employes a feature reconstruction module to guide the extractor to reconstruct missing semantics. Joint learning methods focus on deriving cohesive joint multimodal representations based on inter-modality correlations. For instance, MMIN [76] produces robust joint multimodal representations via cross-modality imagination. However, these methods cannot extract rich sentiment information from incomplete modalities due their inefficient interaction. In contrast, our learning paradigm achieves effective extraction and precise reconstruction of sentiment semantics through complete modality factorization.
2.2 Factorized Representation Learning
The fundamental goal of learning factorized representations is to disentangle representations that have different semanties and distributions. This separation enables the model to more effectively capture intrinsic information and yield favorable modality representations. Previous methods of factorized

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FRF C
Tramnnn Data Floe
Complees Sample (Ongina)
Teacher Branch
Maliorzade repremertianee
3ISM
HMI
Kagaa
Case:
Student Branch
Figure The structure of our HRLF, which consists of three core components: Fine- Fine-grained Representation Factorization (FRF) module, Hierarchical Mutual Information (HMI) maximization mechanism, and Hicrarchical Adversarial Learning (HAL) mechanism.
representation learning primarily rely on auto-encodery [3] and generative adversarial networks [32]. For example, FactorVA [18] introduced to achieve factorization by leveraging the characteristic that representations are both factorial and independent in dimension. Recently, factorization learning has been progressively utilized in MSA tasks [54, 25, 57]. For instance, FDMER [54] utilizes consistency and discreteness constraints between modalities to disentangle modalities into modallty-invariian and modality-privall features. DMD [25] disentangles each modality into modality-independee modality-exclusivg representations and then implements knowledge distillation strategy among the representations with dynamic graphs. MFSA [57] refines multimodal representations and learns complementary representations across modalities by learning modality-specife and modality-agnostic representations. Despite the progress these studies have brought to MSA. certain limitations persist: (i) The supervision of the factorization process is coarse- -raaned and insufficient. (ii) Focusing solely on factorizing distinct representations at the modality level, without taking into account sentimentally beneficial and relevant representations. By contrast, the proposed method decomposes sentiment- relevant representations precisely through intra- and inter-modality translation and sentiment semantic reconstruction. Furthermore, hierarchical mutual information maximization and adversarial learning paradigms are employed to refine and optimize the representation of factorization at the semantic level and the distributional level, respectively, thus yielding robust joint multimodal representations.
and
2.3 Knowledge Distillation
Knowledge distillation leverages additional supervisory signals from a pre-trained teacher network to aid training a student network [15]. There are generally two categories of knowledge distillation methods: distillation from intermediate features [13, 14, 19, 33, 35, 39, 45, 43, 65, 73] and distillation from logits [6. 10, 30, 52, 75]. Many studies [5, 17, 38, 21, 47, 51] utilize knowledge distillation for MSA tasks with missing modalities. These approaches aim transfer dark knowledge from teacher networks trained on complete modalities to student networks trained by missing modalities. The teacher network typically provides richer and more comprehensive feature representations than the student network. For instance, KD-Net [17] utilizes a teacher network with complete modalities to supervise the unimodal student network at both the feature and logits levels. Despite their promising results, these methods neglect precise supervision of representations, resulting in low-quality knowledge transfer. To this end, we implement hierarchical semantic and distributional alignment of the multi-scale representations of both networks to transfer knowledge effectively.
3 Methodology
3.1 Problem Formulation
Given multimodal video segment with three modalities as S [X2, XA, Xv]. where X xdy denote language, audio, and visual modalities, x XA TaxxA and Xv IR Tvxy
3

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respectively. μ= [L, = A. denotes the set of modality types. (-) is the sequence length and (-) is the embedding dimension, where m É p.. We define two missing modality cases simulate the most natural and holistic challenges in real-world scenarios: (1) intra-modality missingness, which indicates some frame-level features in the modality sequences are missing. (2) inter-modality missingness, which denotes some modalities are entirely missing. We aim to recognize the utterance- level sentiments using incomplete multimodal data.
3.2 Overall Framework
Figure 2 illustrates the main workflow of HRLF. The teacher and student networks adopt consistent structure but have different parameters. During the training phase, the workflow of our HRLF is as follows: (i) We first train the teacher network with complete-modality samples and their sentiment labels. (ii) Given video segment sample S, we generate
miising-modallin sample š with the Modality Stochastic Missing (MSM) strategy. MSM simultaneously performs intra-modality missingness and inter-modality missingness. S and S are fed into the pre-trained teacher network and the initialized student network, respectively. (iii) We input each sample into the FRF module, to factorize each modality into a sentiment-relevant representation Qm and a modality-specific representation Un. where m E p. (iv) Sequences [Ct, CA. Cvl and [C', C,a, CVy are generated by concatenating Qm and Um. from all modalities in the teacher and student networks, Each element of the sequences is concatenated yield the joint multimodal representations H' and H (v) The multi-scal. representations of both networks are obtained by passing H' and H through the fully connecco layers. The proposed HMI and HAL are used to align the semantics and distribution between the multiscale representations. (vi) The outputs H' and H' of the fully-comnected layers are fed into the task-specific classifier to get logits L' and L". We constrain the consistency between logits and utilize L' to implement the sentiment prediction. In the inference phase, testing samples are only fed into the student network for downstream tasks.
3.3 Fine-grained Representation Factorization
Modality missing leads to ambiguous sentiment cues in the modality and information redundancy in multimodal fusion. It hinders the model from capturing valuable sentiment semantics and filtering sentiment irrelevant information. Although previous studies in MSA [12, 54] decompose the task- relevant semantics contained in the modality to some extent via simple auto-encoder networks with reconstruction constraints, their purification of sentiment semantics is inadequate, and they cannot be applied to modality missing scenarios. Therefore, we propose Fine-grained Representation Factorization (FRF) module to capture sentiment semantics in modalities. The core idea is to factorize each modality representation into two types of representations:
(1) sentiment-relevant representation, which contains the holistic sentiment semantics of the sample. is modality- modality-indepennde independent, shared across all modalities of the same subject, and robust to modality missing situations. (2) modality- specific representation, which represents modality-specifft task- indeeeedendd information.
As shown in Figure 2, FRF receives the multimodal sequences [X,, XA, Xv] with modality number n 3. The modality X. with a E y passes through a ID temporal convolutional layer with kernel size 3 x 3 and adds the positional embedding [46] to obtain the preliminary representations, denoted as R. = W3x3 (X.) + (E d) E RT.x4. The R. is fed into a Transformer [46] encoder (.). and the last element of its output is denoted as Z. = Fa (Ra) Rdd, The Z. E Z the low-level modality representation of the modality a. We aim to factorize modality representation Z, into a contiment-relevand representation Qa by sentiment encoder Qa = E (Z.) and a modality- specific representation U. by modality encoder U,, ENd (Z.). ). E. and e (-) are composed of multi-layer perceptrons with the ReLU activation. The following two processes ensure adequate factorization and semantic reinforcement of the above two representations.
Intra- and Inter-modality Translation. The proposed FRF effectively decouples sentiment-relevan and modality-speciff representations by simultancously performing intra- and mnter-modality transla- tions. Given a pair of representations Q., and U3 factorized by Z,, and Z3 with o, E ju, the decoder (.) is supposed to translate and synthesize the representation Zaz whose reconstructed domain corresponds to the modality representation Z3 E gg. The (.).) consists of feed-forward neural layers. The modality translations include intra-modality translation (i.e., a = 99) and inter-modality translation (i.e., * B), whose losses are respectively denoted as:

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Lself
-
Σ
Ez.
[| Zo 1
(1)
n
acp
Coross
E E oEp BEN,5BD
Z 1 2g1121,
n
where Zaz = DE Za), (Z))). The translation loss is denoted as: Lirane Cieu
ttans
Sentiment Semantic Reconstruction. To ensure that the reconstructed modality still contains the sentiment semantics from the original modality, we encourage both maintain the consistency of sentiment-relevant semantics and utilize the following loss for constraints, denoted as:
1 ΣΣEz,
[ 80 Qa1la)
Crecon
Np..
n
aεE BE
where = (D,
(Eg Zz)) EM (Za)) is the sentiment-rrlevant representation derived from the reconstructed modality representation. Consequently, the final loss of the FRF is denoted as:
L = Lirans + Lrecon.
3.4 Hierarchical Mutual Information Maximization
The underlying assumption of knowledge distillation is that layers in the pre-trained teacher network can represent certain attributes of given inputs that exist in the task [15]. For successful knowledge transfer, the student network must learn incorporate such attributes into its own learning. Neverthe- less, previous studies [17, 38, 21] based on knowledge distillation simply constrain the consistency between the features of both networks and lack consideration of the intrinsic semanties and inherent properties of the features, leading to semantic misalignment. From the perspective of information theory [1], semantic alignment and attribute mining of representations can be characterized main- taining high mutual information among the layers of the teacher and student networks. We construct Hierarchical Mutual Information (HMI) maximization mechanism implement sufficient semantic alignment and maximize mutual information. The core idea is to progressively align the semantics of representations through hierarchical learning paradigm.
Specifically, the centiment-relevee and modality specific representations Qm and Um of all modal- ities for teacher and student networks are concatenated to obtain the sequences [CL, CA. Cv] and [C C'A C. Each element of the sequences is concatenated to yield the joint multimodal representations H' and H The fully-connected layers are utilized to refine the representation H" E 3d3d with st, yielding H 3d Moreover, we obtain the intermediate multi-scale representations of all layers, denoted I E R2d, I E RR, and I E R2dd For the above five representations, we concatenate features of the same scale to obtain multi-scale representations ET 3d Exd E 2d and Ey E Rdd which are utilized in the subsequent computation.
To estimate and compute the mutual information between representations, we define two random variables X and Y. The P(X) and P(Y) ) are the marginal probability density function of X and Y. The joint probability density function of X and Y is denoted as P(X, Y). The mutual information of the random variables X and Y is represented as:
p(æ, y) p(z)p())
I(X;Y) = Ep(a.))) log
(5)
We only need to obtain the maximum value of the mutual information, without focusing on its exact value. Referring to Deep InfoMax [16]. we estimate the mutual information between variables based
on the Sensen-Shanne Divergence (JSD). The mutual information maximization issue translates into minimizing the JSD between the joint distribution p(z, y) and the marginal distribution p(æ) (x)))))).
1 (DKz 4) m) DxI(v(x))(x)) [mm)
JSD(p((, vi)l:ll)lpl()))))
(6)
where m = (p(x, y) + p()) and DrL L. is Kullback-Leibhe divergence. Mutual information maximization is achieved by maximizing the dyadic lower bound of JSD, denoted
M1(x, y) (-spp b)] --spepssssssspsllieee y)],
(7)
5

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where sp(w) log( + e²r) and (2. y) is the statistics network which is neural network with parameters 6. HMI maximizes the mutual information between hierarchical representations in knowledge distillation, whose optimization loss is expressed as:
3 CuMI Σ
MI(E), E,).
(8)
3.5 Hierarchical Adversarial Learning
Considering that the teacher network has more robust and stable representation distributions, we also need to encourage the alignment of representation distributions in the latent space. Traditional methods [38. 17, 21] simply minimize the KL. divergence between both networks, which easily disturbs the underlying learning of the student network in the deep layers, leading to confounded distributions and unrobust, joint multimodal representations.
To this end, we propose a Hierarchical Adversarial Learning (HAL) mechanism for incrementally aligning the latent distributions between representations of student and teacher networks. The central principle is that the student network tries generate representations to mislead the discriminator De(.) while D.).) discriminates between the representations of the student and teacher networks. In practice, D-(-) is the fully-connected layers. Specifically, given multi-scale representations of EY 3d ET E Id2d, and Ex E IRd with E {1, s), we implement adversarial learning on the same-scale representations of the teacher and student networks to hierarchically supervise consistency. The objective function of HAL is formatted as:
CHAL =
Σ log(1 D,(E,)) +
3.6 Optimization Objectives
The H and H' of the teacher and student networks fed into their task-specific classifiers to produce logits L' and L", respectively, and the consistency of both constrained with KL divergence loss, denoted as CKL=
LL, D*). The L is used for sentiment recognition and supervised with task loss, represented as Leask- For the classification and regression tasks, we use cross-entropy and MSE loss the task losses, respectively. The overall training objective Liotal is expressed as Ltask + LFRF + LHMI + LHAL + LKL.
Lioral =
4 Experiments
4.1
Datasets and Evaluation Metrics
We conduct our experiments on three MSA benchmarks, including MOSI [71], MOSEI [72], and IEMOCAP [4]. The experiments are performed under the word-aligned setting. MOSI is realistic dataset for MSA. It comprises 2,199 short monologue video clips taken from 93 YouTubl movie review videos. There are 1,284, 229, and 686 video clips in train, valid, and data, respectively. MOSEI is dataset consisting of 22,856 movie review video clips, which has 16,326, 1,871, and 4,659 samples in train, valid, and test data. Each sample of MOSI and MOSEI is labelled by human annotators with sentiment score of -3 (strongly negative) to +3 (strongly positive). On the MOSI and MOSEI datasets, we utilize two evaluation metrics, including the Mean Absolute Error (MAE) and F1 score computed for positive/negative classification results. The IEMOCAP dataset consists of 4,453 samples of video clips. Its predetermined data partition has 2.717, 798, and 938 samples in train, valid, and test data. As recommended by [48], four emotions (i.e., happy, sad, angry, and neutral) are selected for emotion recognition. The Fl score is used as the metrie.
4.2 Implementation Details
Feature Extraction. The Glove embedding [36] used to convert the video transcripts to obtain 300-dimensional vector for the language modality. For the audio modality, employ the COVAREP

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Sel-MM
CubeMLP
DMD
MCTN
Tran
SMIL
GCNe
CorriD
HRLF
(Neutrol)
F (Se))
Mixsin Radis
Miniag Radto
Mistng Ratie
Maxing Rattia
Figure 3:
Comparison results of intra-modality missingness on IEMOCAP. We report on the F1 score metric for the happy, sad, angry, and neutral categories.
toolkit [7] to extract 74- 14-dimensional acoustic features, including 12 Mel-frequency cepstral coeffi- cients, voiced/unvoiced segmenting features, and glottal source parameters. For the visual modality, we utilize the Facet [2] to indicate 35 facial action units that record facial movement.
Experimental Setup. Regarding the MOSI [71] and MOSEI [72] datasets, we the aligned multimodal sequences therein (e.g., all sequences of modalíties have length 300) as the original input for the HRLF All models are built the Pytorch [34] toolbox with four NVIDIA Tesla V100 GPUs. The Adam optimizer [20] is employed for network optimization. For MOSI, MOSEL, and IEMOCAP, the detailed hyper-parameter settings are as follows: the learning rates are {le 3, 2e 3, 4e 3}. the batch sizes are {128, 16, 32}, the epoch numbers are {50, 20, 303, and the attention heads are {10, 8, 10). The embedding dimension is 40 on all three datasets. The raw features at the modality missing positions are replaced by zero vectors. For a fair comparison, we re- re- re-implement the State- Of-The-Art (SOTA) methods and combine them with our experimental paradigms. All experimental results are averaged over multiple experiments using five different random seeds.
4.3 Comparison with State-of-the-art Methods
We conduct a comparison between HRLF and eight representative, reproducible state-of-the-art (SOTA) methods, including complete-modality methods: Self-MM [66], CubeMLP [42], and DMD [25], and missing-modaie and CorrKD [24]). and generative methods (i.e., SMIL [29] and GCNet [26]). The extensive experiments are designed to comprehensively assess the robustness and effectiveness of HRLF in scenarios involving both intra-modality and inter-modality missingness.
methods: 1) joint learning methods (i.e., MCTN [37]. TransM [50],
Robustness to Intra-modality Missingness. We simulate intra-modality missingness by ran- domly discarding frame-level features in sequences with ratio E {0.1, 1.0). To vi- sualize the robustness of all models, Figure and 4 show the performance curves of the mod- els for different ratios p. We have the following important observations. (i) As the ratio p in- creases, the performance of all models declines. This phenomenon demonstrates that intra-modality missingness leads significant sentiment semantic loss and fragile multimodal representations.
(ii) Compared to complete- complete-modality meth-
ods, our HRLF demonstrates notable per- formance advantages in missing- moddllit testing conditions and competitive perfor- mance in complete-modas tions. This is because camplete-modalig methods rely on the assumption of data completeness, while training paradigms for missing modalities excel in capturing and reconstructing valuable sentiment seman- tics from incomplete multimodal data. (ii) In contrast to the missing-modalle modality meth- ods, our HRLF demonstrates the highest level of robustness. Through the purification of sentiment semantics and the dual alignment of repre- sentations, the student network masters the core competencies of precisely reconstructing missing semantics and generating robust multimodal representations.
Self-MM
CubeMLP
DMD
MCTN
Trans
SMIL
GCNet
-CorrkD
HRLF
testing condi-
F1
F1 Score
a1 R.4. BR 6A. BE Misiin Ralio
6.4. RA RS 83 Misinu Ratio
Figure 4: Comparison results of intra mntra-modality miss- ingness on (a) MOSI and (b) MOSEI.
Robustness to mnter-modality Missingness. To simulate the case of inter-modality missingness, we remove certain entire modalities from the samples. Tables and contrast the models' resilience to inter-modality missingness. The notation "(1)" signifies that only the language modality is available.
7

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Table I: Comparison of performance under six possible testing conditions of inter-modale miss- ingness and the complete-modality modality testing condition on the MOSI and MOSEI datasets. T-test is conducted on "Avg." column. indicates that 0.05 (compared with the SOTA CorrKD).
Testing Conditions
Datasets
Models
[1]
[o]
[r]
[1
[a.1]
Avg.
a,
Self-MM [66]
67.80
40.95
38.52
69.81
74.97
47.12
56.53
84.64
CubeMLP [42]
64.15
38.91
43.24
63.76
65.12
47.92
53.85
84.57
DMD [25]
68.97
43.33
42.26
70.51
68.45
50.47
57.33
84,50
MCTN [37]
75.21
59.25
58.57
74.82
64.21
68.31
80.12
MOSI
TransM [30]
77.64
63.57
56.48
82.07
80.90
67.24
71.32
82.57
SMIL [29]
78.26
67.69
59.67
79.82
79.15
71.24
72.64
82.85
GCNet [26]
80.91
65.07
58.70
84.73
83.58
70.02
73.84
CorrKD [24]
81.20
66.52
60.72
83,56
82,41
73.74
74.69
83,94
HRLF (Ours)
83.36
69.47
64.59
83.82
83.56
75.62
76.74*
84.15
Self-MM [66]
71,53
43.57
37.61
75.91
74.62
49.52
58.79
83.69
CubeMLP [42)
67.52
39.54
32.58
71.69
70.06
48.54
54.99
83.17
DMD [25]
70.26
46.18
39.84
74.78
72.45
52.70
59.37
84,78
MCTN [37]
75.50
62.72
59.46
76.64
64.84
81.75
MOSEI
TransM [50]
77.98
63.68
58.67
80.46
78.61
62.24
70.27
81.48
SMIL [29]
76.57
65.96
60.57
77.68
76.24
66.87
70.65
80.74
GCNet [26]
80.52
66.54
61.83
81.96
81.15
69.21
73.54
CorrKD [24]
80.76
66.09
62.30
81.74
81,28
71.92
74.02
82,16
HRLF (Ours)
82.05
69.32
64.90
82.62
81.09
73.80
75.63*
82,93
while the audio and visual modalities are missing. "[1, a, u)" denotes the complete-modality testing condition where all modalities available. "Avg." indicates the average performance across six missing-modality lesting conditions. We have the following key findings: (i) The inter-modality miss- ingness leads decline performance for all models, indicating that integrating complementary information from diverse modalities enhances the sentiment semantics within joint representations. (ii) Across all six testing conditions involving inter-modality missingness, demonstrates superior performance among the majority of metrics, affirming its robustness. For example, on the MOSI dataset, HRLF's average Fl score is improved by 2.05% compared CorrKD, and in particular by 3.87% in the testing condition where only visual modality is available (i.e., {v)). The advantage comes from its learning of fine-grained representation factorization and the hierarchical semantic alignment and distributional alignment. (iii) In unimodal testing scenarios, HRLF's performance using only the language modality significantly exceeds other configurations, showing performance similar to that of the complete-modalic setup. In bimodal testing scenarios, configurations involving the language modality exhibit superior performance, even outperforming the complete-me moddlltty setup in specific metrics. This phenomenon underscores the richness of sentiment semantics within the language modality and its dominance sentiment inference and missing semantic reconstruction processes.
HRLF consistently
4.4 Ablation Studies
To affirm the effectiveness and indispensability of the module and mechanisms and strategies proposed in HRLF, we perform ablation experiments under two missing-modalit scenarios on the MOSI dataset, as shown in Table and Figure 5. We have the fol-
F1 Score
lowing important observations: (i) First, when the FRF is removed, sentiment-relevan and modality- specific information in the modalities are confused, hindering sentiment recognition and leading to sig- nificant performance degradation. This phenomenon demonstrates the effectiveness of the proposed repre- sentation factorization paradigm for adequate capture of valuable sentiment semantics. (ii) When HMI
Missing Ratio
Figure 5: Ablation results of intra- modality
missingness case on the MOSI dataset.

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Table 2: Comparison of performance under six possible testing conditions of inter-modality missing- ness and the complete-modallee testing condition on the IEMOCAP dataset. T-test is conducted on "Avg." column. indicates that p < 0.05 (compared with the SOTA CorrKD).
Testing Conditions
Models
Metrics
(1)
(a]
(v)
(1,a)
[1,
(a,
Avg.
{1, v)
Happy
66.9
52.2
50.1
69,9
68.3
56.3
60.6
90.8
Sad
68.7
51.9
54.8
71.3
69.5
57.5
62.3
86.7
Self-MM [66]
Angry
65.4
67.7
56.6
60.7
Neutral
55.8
48.2
50.4
58.1
56.5
52.8
53.6
72.7
Happy
68.9
54.3
51.4
72.1
69.8
60.6
62.9
89.0
Sad
65.3
54.8
53.2
70.3
68.7
58.1
61.7
88.5
CubcMLP [42]
Angry Neutral
65.8
53.1
50.4
69.5
69.0
54.8
60.4
87.2
53.5
50.8
48.7
57.3
54.5
51.8
52.8
71.8
Happy
69.5
55.4
51.9
73.2
70.3
61.3
63.6
91.1
Sad
65.0
54.9
53.5
70.7
69.2
61.1
62.4
88.4
DMD [25]
Angry
64.8
$3.7
69.9
57.2
61.3
88.6
Neutral
54.0
51.2
48.0
56.9
55.6
53.4
53.2
72.2
Happy
76.9
77.6
66.9
70.9
83.1
Sad
76.7
64.4
60.4
78.9
77.1
68.6
71.0
82.8
MCTN [37]
Angry
77.1
61.0
56.7
81.6
80.4
58.9
69.3
84.6
Neutral
60.1
51.9
50.4
64.7
62.4
54.9
57.4
67.7
Happy
78.4
64.5
61.1
81.6
80.2
66.5
72.1
85.5
Sad
79.5
63.2
58.9
82.4
80.5
64.4
71.5
84.0
TransM [50]
Angry
81.0
60.7
83.9
81.7
66.9
73.2
86.1
Neutral
60.2
49.9
50.7
65.2
62.4
52.4
56.8
67.1
Happy
80.5
66.5
81.8
68.2
74.0
86.8
Sad
78.9
65.2
62.2
82.4
79.6
68.2
72.8
85.2
SMIL [29]
Angry
79.6
67.2
61.8
83.1
82.0
67.8
73.6
84.9
Neutral
60.2
50.4
48.8
65.4
62.2
52.6
56.6
68.9
Happy
81.9
67.3
66.6
83.7
82.5
69.8
75.3
87.7
Sad
80.5
69.4
66.1
83.8
81.9
70.4
75.4
86.9
GCNet [26]
Angry
80.1
81.6
73.8
85.2
Neutral
61.8
51.1
49.6
66.2
63.5
53.3
57.6
71.1
Happy
82.0
70.0
76.1
87.5
Sad
82.7
71.3
67.6
83.4
82,2
72.5
76.6
85.9
Cor<KD [24]
Angry
82.2
67.0
65.8
83.9
82.8
67.3
74.8
86.1
Neutral
63.1
54.2
52.3
68.5
64.3
57.2
59.9
71.5
Happy
84.9
71.8
69.7
86.4
85.6
72.3
78.5"
88.1
Sad
83.7
71.1
69.0
85.3
83.9
73.6
77.8"
86.4
HRLF (Ours)
Angry
83.4
69.1
67.2
83.5
70.9
76.4"
86.7
Neutral
66.8
56.1
54.5
68.9
67.0
56.9
61.7"
71.3
is climinated, the worse performance demonstrates that aligning the high-level semantics in the representation by maximizing mutual information can generate favorable joint representations for the student network. (iii) Finally, we remove HAL,, and the declined results illustrate that multi-scale adversarial learning can effectively align the representation distributions of student and teacher net- works, thus effectively constraining the consistency across representations. This paradigm facilitates recovery missing semantics.
4.5 Qualitative Analysis
To intuitively show the robustness of the proposed framework against modality missingness, we randomly select 100 samples in each emotion category on the IEMOCAP testing set to perform the visualization evaluation. The comparison models include CubeMLP [42] (complete-modality method). TransM [50] (joint learning-based missing- modality method), and GCNet [26] (generation- based biising--mdallin method). (i) As shown Figure 6, CubeMI.P P fails cope with the missing modality challenge because representations with different emotion categories heavily confounded,

---

Table 3: Ablation results of inter-modality missingness the MOSI dataset.
Testing Conditions
Models
[4]
{a}
[u]
[[,a]
[1,x]
[0,0]
Avg.
[1,0,
HRLF (Full)
83,36
69.47
64.59
83.82
83.56
75.62
76.74
84.15
w/o FRF
80.85
67.06
61.78
81.94
81.38
73.58
74.43
82.76
w/o HMI
81.54
67.72
62.70
82.45
74.22
75.09
83.25
w/o HAL
82.03
63.11
83.12
82.67
74.59
75.60
83.67
Happy
Angry
Neutral
Sad
(a) CubeMLP
(b) TransM
(c) GCNet
(d) HRLF
Figure 6: Visualization of representations from different methods with four emotion categories on the IEMOCAP testing set. The default testing conditions contain intra-modality missingness (i.e., missing rate p 0.5) and inter-modality missingness (i.e., only the language modality is available).
leading to the worst results. (ii) Although TransM and GCNet mitigate the indistinguishable emotion semantics to some extent, their performance sub-optimal since the distribution boundaries of the different emotion representations enables representations belonging to the same emotion category to form compact clusters, while representations of different categories well separated. The above phenomenon benefits from the effective extraction of sentiment semantics and the precise filtering of task redundant information by the proposed hierarchical representation learning framework, which results in better joint multimodal representations. This further confirms the robustness and superiority our framework.
generally ambiguous and coupled. (iii) In comparison, our HRLF
Conclusion and Discussion
In this paper, present a Hicrarchical Representation Learning Framework (HRLF) to address diverse missing modality dilemmas the MSA task. Specifically, we mine sentiment-relevan repre- sentations through fine-grained representation factorization module. Additionally, the hierarchical mutual information maximization mechanism and the hierarchical adversarial learning mechanism are proposed for semantic and distributional alignment of representations of student and teacher networks to accurately reconstruct missing semantics and produce robust joint multimodal representations. Comprehensive experiments validate the superiority of our framework.
Discussion of Limitation and Future Work. The current method defines the modality missing cases both moter-modality missingness and intra-modie aoda-mod missingness. Nevertheless, in real- world applications, modality missing cases may be very intricate and difficult to simulate. Consequently, the proposed method may suffer some minor performance loss when applied to real-world scenarios. In the future, we will explore more intricate modality missing cases and design suitable algorithms to compensate for this deficiency.
Discussion of Broad Impacts. The positive impact of our approach lies in the ability to significantly improve the robustness and stability of multimodal sentiment analysis systems against heterogeneous modality missingness in real-world applications. Nevertheless, this technology may have a negative impact when it falls into the wrong hands, e.g., the proposed model is used for malicious purposes by injecting biased priors to recognize the emotions of specific groups
Acknowledgements
This work was supported in part by National Key R&D Program of China 2021ZD01 13502 and part by Shanghai Municipal Science and Technology Major Project 20200022X00003..
10

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References
[1] Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D Lawrence, and Zhenwen Dai. Vari- ational information distillation for knowledge transfer. In Proceedings the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9163-9171, 2019.
[2] Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency. Openface: an open source facial behavior analysis toolkit. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1-10. IEEE, 2016. 7
[3] Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan. Domain separation networks, Conference on Neural Information Processing Systems (NeurIPS), 29, 2016. 3
[4] Carlos Busso, Murtaza Bulut, Chi-Chun Lec, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jeannette N Chang. Sungbok Lee, and Shrikanth S Narayanan. Iemocap: Interactive emotional dyadic motion capture database. Language Resources and Evaluation, 42:335-359, 2008. 6
[5] Jac Won Cho, Dong-Jin Kim, Jinsoo Choi, Yunjae Jung, and In So Kweon. Dealing with missing modalities the visual question mifwer-difference prediction task through knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1592-1601, 2021.
[6] Jang Hyun Cho and Bharath Hariharan. On the efficacy of knowledge distillation. In Proceedings of the IEEE/CVF International Conference Computer Vision (ICCV), pages 4794 4802, 2019. 3
[7] Gilles Degottex, John Kane, Thomas Drugman, Tuomo Raitio, and Stefan Scherer. Covarep collaborative voice analysis repository for speech technologies. In 2014 leee International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 960-964. IEEE, 2014.
[8] Changde Du, Changying Du Hao Wang, Jinpeng Li, Wei-Long Zheng, Bao-Liang Lu, and Huiguang He. Semi-supervised deep generative modelling of incomplete multi-modality emotional data. In Proceedings of the 26th ACM international conference on Multimedia (ACM MM), pages 108 116, 2018.
[9] Yangtao Du, Dingkang Yang, Peng Zhai, Mingchen Li, and Lihua Zhang. Learning associative representation for facial expression recognition. In IEEE International Conference Image Processing (ICIP), pages 889-893, 2021.
[10] Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar. Born again neural networks. In International Conference on Machine Learning (ICML), pages 1607-1616. PMLR, 2018.
[11] Wei Han, Hui Chen, and Soujanya Poria. Improving multimodal fusion with hicrarchical mutual information maximization for multimodal sentiment analysis. arXiv preprint (rXiv:2109.00417 2021.
[12] Devamanyu Hazarika, Roger Zimmermann, and Soujanya Poria. Misa: Modality-invee invariant and-specific representations for multimodal sentiment analysis. In Proceedings of the 28th ACM International Conference on Multimedia (ACM MM), pages 1 1122-1131, 2020. 1, 2,
[13] Byeongho Hco, Jeesoo Kim, Sangdoo Yun, Hyojin Park, Nojun Kwak, and Jin Young Choi. A comprehensive overhaul of feature distillation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 1921-1930, 2019.
[14] Byeongho Heo, Minsik Lee, Sangdoo Yun, and Jin Young Choi. Knowledge transfer via distillation of activation boundaries formed by hidden neurons. In Proceedings of the AAAI Conference Artificial Intelligence (AAAI), volume 33, pages 3779-3787, 2019.
[15] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint 1rXivv1503.025333 2015. 3.5

---

[16] R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Matii Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv: 066800667, 2018. 5
[17] Minhao Hu, Matthis Maillard, Ya Zhang, Tommaso Ciceri. Giammarco La Barbera, Isabelle Bloch, and Pietro Goni. Knowledge distillation from multi-modal to mono-modal segmentation networks. In Medical Image Computing and Computer Assisted Intervention-MIC 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part I 23, pages 772-781. Springer, 2020. 3, 5,6
2020:
[18] Hyunjik Kim and Andriy Mnih. Disentangling by factorising. In International Conference on Machine Learning (ICML), pages 2649-2658. PMLR, 2018. 3
[19] Jangho Kim, SeongUk Park, and Nojun Kwak. Paraphrasing complex network: Network compression via factor transfer. Advances in Neural Information Processing Systems (NeurIPS), 31, 2018. 3
[20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.690 2014. 7
[21] Saurabh Kumar, Biplab Banerjee, and Subhasis Chaudhuri. Online sensor hallucination via knowledge distillation for multimodal image classification. arXiv preprint arXiv:1908 10559, 2019. 5,5, 6
[22] Mingcheng Li, Dingkang Yang, Yuxuan Lei, Shunli Wang, Shuaibing Wang, Liuzhen Su, Kun Yang, Yuzheng Wang, Mingyang Sun, and Lihua Zhang. A unified self-distillation framework for multimodal sentiment analysis with uncertain missing modalities. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), volume 38, pages 10074-10082, 2024. 2
[23] Mingcheng Li, Dingkang Yang, and Lihua Zhang. Towards robust multimodal sentiment analysis under uncertain signal missing. IEEE Signal Processing Letters, 30:1497-1501, 2023. 2
[24] Mingcheng Li, Dingkang Yang, Xiao Zhao, Shuaibing Wang, Yan Wang, Kun Yang. Mingyang Sun, Dongliang Kou, Ziyun Qian, and Lihua Zhang. Correlation-decouplee knowledge distil- lation for multimodal sentiment analysis with incomplete modalities. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12458- 12468, 2024. 7, 8.9
[25] Yong Li, Yuanzhi Wang. and Zhen Cui. Decoupled multimodal distilling for emotion recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6631-6640, 2023. 1, 3,8,888,9
[26] Zheng Lian, Lan Chen, Licai Sun, Bin Liu, and Jianhua Tao. Genet: graph completion network for incomplete multimodal learning in conversation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 2, 7,8.9 9
[27] Zhizhong Liu, Bin Zhou, Dianhui Chu, Yuhang Sun, and Lingqiang Meng. Modality translation- based multimodal sentiment analysis under uncertain missing modalities. Information Fusion, 101:101973, 2024.
[28] Wei Luo, Mengying Xu, and Hanjiang Lai. Multimodal reconstruct and align net for missing modality problem in sentiment analysis. In International Conference on Multimedia Modeling, pages 41 1-122 Springer, 2023. 2
[29] Mengmeng Ma, Jian Ren, Long Zhao, Sergey Tulyakov, Cathy Wu, and Xi Peng. Smil: Multimodal learning with severely missing modality. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), volume 35, pages 2302-23022 2021. 2, 8,9
[30] Seyed Iman Mirzadeh, Mehrdad Farajtabar, Ang Li, Nir Levine, Akihiro Matsukawa, and Hassan Ghasemzadeh. Improved knowledge distillation via teacher assistant. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), volume 34, pages 5191-5198, 2020. 3
12

---

[31] Louis-Philippe Morency, Rada Mihalcea, and Payal Doshi. Towards multimodal sentiment analysis: Harvesting opinions from the web. In Proceedings the 13th International Conference on Multimodal Interfaces, pages 169-176, 2011.
[32] Augustus Odena, Christopher Olah, and Jonathon Shlens. Conditional image synthesis with auxiliary classifier gans. In International Conference on Machine Learning (ICML), pages 2642-2651. PMLR, 2017.
[33] Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho. Relational knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition (CVPR), pages 3967-3976, 2019.
[34] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang. Zachary De Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017. 7
[35] Baoyun Peng, Xiao Jin, Jiaheng Liu, Dongsheng Li. Yichao Wu, Yu Liu. Shunfeng Zhou, and Zhaoning Zhang. Correlation congruence for knowledge distillation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 5007-5016, 2019. 3
[36] Jeffrey Pennington, Richard Socher, and Christopher D Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532-1543, 2014. 6
[37] Hai Pham, Paul Pu Liang, Thomas Manzini, Louis
Louis-Philippe Philippe Morency, and Barnabás Póczos. Found in translation: Learning robust joint representations by cyclic translations between modalities. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), volume 33,
pages 6892-6899, 2019. 2, 7, 8,
[38] Masoomeh Rahimpour, Jeroen Bertels, Ahmed Radwan, Henri Vandermeulen, Stefan Sunaert, Dirk Vandermeulen, Frederik Maes, Karolien Goffin, and Michel Koole. Cross-modal distillation to improve mri-based brain tumor segmentation with missing mri sequences. IEEE Transactions on Biomedical Engineering, 69(7):2133-214 2021. 5,
[39] Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.65500, 2014. 3
[40] Roee Shraga, Haggai Roitman, Guy Feigenblat, and Mustafa Cannim. Web table retrieval using multimodal deep learning. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1399-1408, 2020.
[41] Matthias Springstein, Eric Müller-Budack, and Ralph Ewerth. Quti! quantifying text-image consistency in multimodal documents. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2575-2579, 2021.
[42] Hao Sun, Hongyi Wang, Jiaqing Liu, Yen-Wo Sen-Wei Chen, and Lanfen Lin. Cubemlp: An mip-based model for multimodal sentiment analysis and depression estimation. In Proceedings of the 30th ACM International Conference on Multimedia (ACM MM), pages 3722-3722 2022. 2, 7, 8, 9
[43] Yonglong Tian, Dilip Krishnan, and Phillip Isola. Contrastive representation distillation. arXiv preprint arXiv: 19t...11009999 2019. 3
[44] Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J Zico Kolter, Louis-Philippe Morency, and Ruslan Salakhutdinov. Multimodal transformer for unaligned multimodal language sequences. In Proceedings of the conference. Association for Computational Linguistics. Meeting, volume 2019, page 6558. NIH Public Access, 2019. 2
[45] Frederick Tung and Greg Mori. Similarity-preservivi knowledge distillation. In Proceedings of the IEEE/CV International Conference on Computer Vision (ICCV), pages 1365-1374, 2019.
3
[46] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, tukasz Kaiser, and Illia Polosukhin. Attention is all you need, Advances in Neural Information Processing Systems (NeurIPS), 30, 2017. 4
13

---

[47] Hu Wang, Congbo Ma, Jianpeng Zhang, Yuan Zhang. Jodie Avery. Louise Hull, and Gustavo Carneiro. Learnable cross- ross-modal knowledge distillation for multi- learning with missing modality. In International Conference on Medical Image Computing and Computer-Assiste Intervention, pages 216-226. Springer, 2023.
[48] Yansen Wang, Ying Shen, Zhun Liu, Paul Pu Liang, Amir Zadeh, and Louis- Douis-Philippe Morency. Words can shift: Dynamically adjusting word representations using nonverbal behaviors. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), volume 33, pages 7216- 7223, 2019.
[49] Yuanzhi Wang. Zhen Cui, and Yong Li. Distribation-consisteen modal recovering for incomplete multimodal learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 22025-22034, 2023. 2
[50] Zilong Wang, Zhaohong Wan, and Xiaojun Wan. Transmodality: An end2end fusion method with transformer for multimodal sentiment analysis. In Proceedings of The Web Conference 2020, pages 2514-2520, 2020. 2, 8,9
[51] Wenke Xia, Xingjian Li, Andong Deng, Haoyi Xiong, Dejing Dou, and Di Hu. Robust cross- modal knowledge distillation for unconstrained videos. arXiv preprint arXiv:2304.07775, 2023. 3
[52] Chenglin Yang, Lingxi Xie, Chi Su, and Alan L Yuille. Snapshot distillation: Teacher-student optimization in one generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2859-2868, 2019. 3
[53] Dingkang Yang. Zhaoyu Chen, Yuzheng Wang, Shunli Wang. Mingcheng Li, Siao Liu, Xiao Zhao, Shuai Huang, Zhiyan Dong, Peng Zhai, and Lihua Zhang. Context de-confounded emotion recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19005-19015, June 2023. 1
[54] Dingkang Yang, Shuai Huang, Haopeng Kuang, Yangtao Du, and Lihua Zhang. Disentangled representation learning for multimodal emotion recognition. In Proceedings of the 30th ACM International Conference on Multimedia (ACM MM), pages 1642-1651, 2022. 1, 3,
[55] Dingkang Yang, Shuai Huang, Yang Liu, and Lihua Zhang. Contextual and cross-modal interaction for multi-modal speech emotion recognition. IEEE Signal Processing Letters, 2022. 1
[56] Dingkang Yang. Shuai Huang, Shunli Wang, Yang Liu, Peng Zhai, Liuzhen Su, Mingcheng Li, and Lihua Zhang. Emotion recognition for multiple context awareness, In Computer Vision- ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXVII, pages 144-162. Springer, 2022. 1
[57] Dingkang Yang, Haopeng Kuang, Shuai Huang, and Lihua Zhang. Learning poddlity-sppccinf and-agnostic representations for asynchronous multimodal language sequences. In Proceedings of the 30th ACM International Conference on Multimedia (ACM MM), pages 1708-1717, 2022.
1.3
[58] Dingkang Yang, Haopeng Kuang, Kun Yang, Mingcheng Li, and Lihua Zhang. Towards asynchronous multimodal signal interaction and fusion via tailored transformers. JEEE Signal Processing Letters, 2024. 1
[59] Dingkang Yang, Mingcheng Li, Linhao Qu, Kun Yang, Peng Zhai Song Wang, and Lihua Zhang. Asynchronous multimodal video sequence fusion via learning modality-exclusive aud-agnostic representations. IEEE Transactions on Circuits and Systems for Video Technology. 2024.
[60] Dingkang Yang, Mingcheng Li, Dongling Xiao, Yang Liu, Kun Yang, Zhaoyu Chen, Yuzheng Wang, Peng Zhai, Ke Li, and Lihua Zhang. Towards multimodal sentiment analysis debiasing via bias purification, In Proceedings of the European Conference on Computer Vision (ECCV), 2024. 1
14

---

[61] Dingkang Yang, Yang Liu, Can Huang. Mingcheng Li, Xiao Zhao, Yuzheng Wang, Kun Yang. Yan Wang, Peng Zhai, and Lihua Zhang. Target and source modality co-reinforcement for emotion understanding from asynchronous multimodal sequences. Knowledge-Baon Bassed Systems, 265:1 10370, 2023.
[62] Dingkang Yang. Dongling Xiao, Ke Li, Yuzheng Wang, Zhaoyu Chen, Jinjie Wei, and Lihua Zhang. Towards multimodal human intention understanding debiasing via subject- deconfounding, arXiv preprint arXiv:2403.05023. 2024.
[63] Dingkang Yang, Kun Yang, Haopeng Kuang, Zhaoyu Chen, Yuzheng Wang, and Lihua Zhang. Towards context-awars aware emotion recognition debiasing from causal demystification perspective via de-confounded training. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
[64] Dingkang Yang, Kun Yang, Mingcheng Li, Shunli Wang, Shuaibing Wang, and Lihua Zhang. Robust emotion recognition in context debiasinn. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12447-12457, 2024.
[65] Junho Yim, Donggyu Joo, Jihoon Bae, and Junmo Kim. A gift from knowledge distillation: Fast optimization, network minimization and transfer learning. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4133-4141, 2017. 3
[66] Wenmeng Yu, Hua Xu, Ziqi Yuan, and Jiele Wu. Learning modality-specific representations with self-supervised multi-task learning for multimodal sentiment analysis. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), volume 35, pages 10790-10797, 2021. 2021. 8.9
[67] Ziqi Yuan, Wei Li, Hua Xu, and Wenmeng Yu. Transformer- based feature reconstruction network for robust multimodal sentiment analysis. In Proceedings the 29th ACM International Conference on Multimedia (ACM MM), pages 4400 4407, 2021.
[68] Ziqi Yuan, Yihe Liu, Hua Xu, and Kai Gao. Noise imitation based adversarial training for robust multimodal sentiment analysis. IEEE Transactions Multimediaa 2023. 2
[69] Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Phillppp Morency. Tensor fusion network for multimodal sentiment analysis. arXiv preprint arXiv:
2017. 2
[70] Amir Zadeh, Paul Pu Liang, Navonil Mazumder, Soujanya Poria, Erik Cambria, and Louis- Philippe Morency. Memory fusion network for multi-view sequential learning. In Proceedings
of the AAAI Conference on Artificial Intelligence (AAAI), volume 32, 2018. 2
[71] Amir Zadeh, Rowan Zellers, Eli Pincus, and Louis- Philippe Morency. Mosi: multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos. arXiv preprint 1606::600.0622. 2016. 6,7
[72] AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2236-2246, 2018. 6,7
[73] Sergey Zagoruyko and Nikos Komodakis. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv: 2016. 3
[74] Jiandian Zeng, Tianyi Liu, and Jiantao Zhou. Tag-assisted multimodal sentiment analysis under uncertain missing modalities. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1545-1554, 2022. 2
[75] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2881-2890, 2017.
[76] Jinming Zhao, Ruichen Li, and Qin Jin. Missing modality imagination network for emotion recognition with uncertain missing modalities. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 1 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2608-2613 2021. 2
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Reviews:
[{"review_id": "mbmGepej3x", "reviewer": "Reviewer_zmpp", "summary": "The paper addresses the challenges of multimodal sentiment analysis (MSA) in real-world applications, particularly when some modalities may be missing, which can hinder the effectiveness of the analysis. The authors propose a framework called Representation Factorization and Alignment (ReFA) to tackle the issue of uncertain missing modalities in MSA. \n\nThe ReFA framework consists of three core components:\n\n1. Fine-grained Representation Factorization (FRF) module: This module extracts valuable sentiment information by factorizing each modality into sentiment-relevant and modality-specific representations through cross-modal translation and sentiment semantic reconstruction.\n\n2. Hierarchical Mutual Information (HMI) maximization mechanism: This mechanism incrementally maximizes the mutual information between multi-scale representations to align and reconstruct the high-level semantics in the representations.\n\n3. Hierarchical Adversarial Learning (HAL) mechanism: This mechanism progressively aligns and adapts the latent distributions of the representations to produce robust joint multimodal representations.\n\nThe authors conducted comprehensive experiments on three datasets, demonstrating that the ReFA framework significantly improves MSA performance under both uncertain missing-modality and complete-modality testing conditions.", "questions": "1. Can the authors provide more details on the computational requirements and training time of ReFA compared to baseline methods?\n2. How does the performance of ReFA change with varying amounts of training data? Is there a minimum data requirement for the framework to be effective?\n3. Have the authors explored the applicability of ReFA to other multimodal tasks beyond sentiment analysis? If not, what modifications might be needed?\n4. Could the authors provide insights into why the language modality seems to be particularly effective in unimodal scenarios?\n5. Are there any scenarios or types of data where ReFA might not perform well? It would be helpful to discuss potential limitations.\n6. How does the framework handle noisy data within the available modalities? Can the authors provide experimental results or discussions on the impact of noisy data on the performance of ReFA?", "limitations": "The paper does not explicitly address limitations or potential negative societal impacts of the work. Some suggestions for improvement:\n1. Societal Impact: While the paper mentions broader impacts and limitations in the appendix, the main text lacks a detailed discussion on potential negative societal impacts. The authors should consider elaborating on ethical concerns, such as the potential misuse of sentiment analysis in sensitive applications or privacy issues related to multimodal data collection.\n2. Discuss potential biases in the datasets used and how they might affect the model's performance across different demographic groups.\n3. Acknowledge any limitations in the generalizability of the results to real-world, non-curated data.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "Originality:\n1. The paper proposes a Representation Factorization and Alignment (ReFA) framework to address multimodal sentiment analysis under uncertain missing modalities.\n2. Introduces innovative components like fine-grained representation factorization, hierarchical mutual information maximization, and hierarchical adversarial learning.\n\nQuality:\n1. Comprehensive experiments on three datasets (MOSI, MOSEI, IEMOCAP) demonstrate significant performance improvements.\n2. Ablation studies validate the effectiveness of each proposed component.\n3. Qualitative analysis with visualizations provides intuitive understanding of the framework's robustness.\n\nClarity:\n1. The paper is well-structured, with clear sections on related work, methodology, and experiments.\n2. Figures and tables effectively illustrate the framework and results.\n\nSignificance:\n1. Addresses an important real-world challenge of missing modalities in multimodal sentiment analysis.\n2. Shows consistent performance improvements over state-of-the-art methods across different missing modality scenarios.\n3. The framework's robustness to both intra-modality and inter-modality missingness enhances its practical applicability.", "weaknesses": "1. The paper lacks a detailed discussion on the computational complexity and runtime performance of the proposed framework compared to existing methods.\n2. While the proposed ReFA framework is innovative, the individual components (such as mutual information maximization and adversarial learning) have been explored in other contexts. The novelty primarily lies in their specific combination and application to MSA with missing modalities.\n3. The paper did not mention the models that were used for the final classification or regression, only mentioned feature extraction models. \n4. The paper doesn't discuss potential limitations of the approach or cases where it might not perform well.\n5. There's no discussion on the framework's generalizability to other multimodal tasks beyond sentiment analysis.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1**: About computational complexities.\n\n**A1**: Thank you for your comments! We need to clarify that we have compared and discussed the proposed framework with the existing methods in terms of the number of parameters, FLOPs, and performance in three testing situations, as shown in Section A.4 of the Appendix. The proposed framework has the strongest robustness against missing modalities but has the lowest FLOPs and a lower number of parameters than most of the methods, which achieves a reasonable trade-off between complexity and performance.\n***\n**Q2**: About the technical contribution of the proposed components.\n\n**A2**: We need to clarify that:\n* Mutual information maximization and adversarial learning are not simply combined and employed but are specifically designed to address the problems and limitations of existing MSA methods under missing modality situations.  Specifically, existing methods lack effective supervision of semantic alignment and distributional alignment during feature reconstruction, resulting in the inability to precisely recover sentiment semantics, as stated in lines 37-38 & lines 87-90 & lines 157-160 & lines 184-188 in the main manuscript. In contrast, the proposed interaction paradigm guides the reconstruction of missing sentiment information both at the semantic level and at the distributional level, thus restoring realistic modality features as much as possible. \n* Our proposed components bring significant performance gains, which are essential in the framework. The ablation results in Section 4.4 in the main manuscript and Section A.3 in the Appendix demonstrate this advantage.\n***\n**Q3**: Description of the models used for the classification or regression.\n\n**A3**: Thank you for your reminder! The models used for classification and regression are fully-connected layers, including two linear layers, a ReLU activation layer, and a Softmax layer (in the case of classification tasks). We promise to add detailed descriptions in the revision.\n***\n**Q4**: Discuss the potential limitations or cases where it might not perform well.\n\n**A4**: \n*  We need to clarify that we have already discussed some of the limitations of this paper, i.e., the framework is based on teacher network trained on complete-modality data, and thus its performance depends to some extent on the upper bound of the teacher network's performance, as stated in A.6 in the Appendix.  In the future, we plan to use the self-distillation paradigm to improve the flexibility and applicability of the model. \n* Furthermore, in real-world applications, modality missing cases can be very intricate and complex, leading to a possible minor loss in model performance. In the future, we will explore more complex modality-missing cases to compensate for this deficiency. We will add this limitation in the revision.\n***\n**Q5**: Discuss the framework generalization to other multimodal tasks.\n\n**A5**: Valuable suggestions! We added comparison experiments of our framework with some of the baselines on humor detection (i.e., UR-FUNNY dataset) and sarcasm discovery (MUSTARD dataset) tasks, as shown in Table. 1 in **reply.pdf**.\nThe testing conditions include Intra-Modality Missingness (Intra-MM), Inter-Modality Missingness (Inter-MM), and Complete Modality (CM). The superior experimental results demonstrate the generalization of the framework to multiple multimodal tasks.\n***\n**Q6**: About ReFA performance with data size.\n\n**A6:** The framework has a strong generalization to datasets of different sizes for the following reasons:\n* We have launched comprehensive experiments on the MOSI, IEMOCAP, and MOSEI datasets of sequentially increasing sizes, as shown in Section 4.3 in the main manuscript. \n* In addition, we have conducted experiments based on different ratios of samples in the MOSI dataset, as shown in Fig. 1 in **reply.pdf**. The experimental results are the average under five different random seeds.\n***\n**Q7**: The reason for Language being effective in unimodal scenarios.\n\n**A7**: Constructive Comments. We provide two insights below: \n* Compared to linguistic modalities, non-linguistic modalities are potentially unreliable because audiovisual feature extraction tools typically introduce additional redundancy and noise. \n* As highly abstract symbolic systems, linguistic modalities typically contain more information and knowledge density, providing more effective structured semantic representations.\n***\n**Q8**: About the noise effect on ReFA performance.\n\n**A8**:  We add various ratios of Gaussian noise to the MOSI dataset, and Fig. 2 in **reply.pdf** demonstrates the robustness of the proposed framework against noisy data, as the factorization mechanism adequately captures sentiment cues and filters out noise. \n*** \n**Q9**: About potential social impacts.\n\n**A9**: Thanks for the comments! We will add a detailed discussion of social implications to the main revision manuscript.\n***\n**Q10**: About potential bias in the dataset.\n\n**A10**: In practice, we observe two possible biases in the datasets, including label bias and context bias.\n* The label bias usually occurs when the number of training samples for a specific category is more significant than for other categories. Such unbalanced data distribution would lead to trained models relying heavily on label bias as statistical shortcuts to make inaccurate predictions across demographic groups. \n* The context bias emerges when trained models exhibit strong spurious correlations between specific categories and context words in language modality. MSA models tend to predict samples containing those words to an incorrect category based on biased statistical information rather than intrinsic textual semantics.\n***\n**Q11**: About result generalizability.\n\n**A11**: Non-curated data in real-world applications may contain more intricate cases of missing modalities, leading to a slight performance loss of the model. We will add this description in the revision.", "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": "5iyHTGA0Ke", "reviewer": "Reviewer_JfYH", "summary": "The paper presents the Representation Factorization and Alignment (ReFA) framework for Multimodal Sentiment Analysis (MSA) under uncertain missing modalities. ReFA employs a fine-grained representation factorization module to extract sentiment-relevant and modality-specific representations through crossmodal translation and sentiment semantic reconstruction. It introduces a hierarchical mutual information maximization mechanism to align and reconstruct high-level semantics incrementally. Additionally, a hierarchical adversarial learning mechanism progressively aligns latent distributions to create robust joint multimodal representations. Experiments on three datasets show that ReFA significantly enhances MSA performance under both uncertain missing-modality and complete-modality conditions.", "questions": "1. The idea and design of Intra- and Inter-modality Translations are sound; however, the implementation of the translation loss is overly simplistic and lacks a task-specific approach, making the methodology appear somewhat naive.\n\n2. Similarly, the Sentiment Semantic Reconstruction section suffers from the same issue, with a basic and unrefined approach that fails to leverage the complexity of the task.\n\n3. Both sections give the impression that while Translations and Reconstruction are being performed, the methods are indistinguishable aside from their goals. This indicates a lack of differentiation in handling the unique characteristics of each type of information.\n\n4. The HMI and HAL components seem to merely apply two loss functions to the multi-scale features of the teacher-student network. This approach is quite common in knowledge distillation and thus lacks significant innovation.\n\n5. Moreover, the paper lacks relevant case studies to validate the effectiveness of the proposed algorithm. There is also an absence of error analysis to identify the limitations and shortcomings of the method.", "limitations": "The current distillation framework relies on a teacher network trained with complete modality data. A potential limitation of this approach is that its applicability to scenarios with missing modalities is inherently constrained by the performance ceiling of the teacher network. This dependency may limit the effectiveness and generalizability of the framework in handling diverse cases of missing modality data.", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Strength:\n\n1. One of the challenges in multimodal sentiment analysis is the potential for missing modality information in real-world scenarios. This study addresses this practical issue by proposing an effective algorithm with notable real-world applicability.\n\n2. The motivation for the research is clearly articulated, pinpointing the shortcomings of existing studies with strong logical coherence.\n\n3. The proposed algorithm achieves state-of-the-art (SOTA) results across relevant datasets, which validates its effectiveness to a significant extent.", "weaknesses": "Weakness:\n\n1. The idea and design of Intra- and Inter-modality Translations are sound; however, the implementation of the translation loss is overly simplistic and lacks a task-specific approach, making the methodology appear somewhat naive.\n\n2. Similarly, the Sentiment Semantic Reconstruction section suffers from the same issue, with a basic and unrefined approach that fails to leverage the complexity of the task.\n\n3. Both sections give the impression that while Translations and Reconstruction are being performed, the methods are indistinguishable aside from their goals. This indicates a lack of differentiation in handling the unique characteristics of each type of information.\n\n4. The HMI and HAL components seem to merely apply two loss functions to the multi-scale features of the teacher-student network. This approach is quite common in knowledge distillation and thus lacks significant innovation.\n\n5. Moreover, the paper lacks relevant case studies to validate the effectiveness of the proposed algorithm. There is also an absence of error analysis to identify the limitations and shortcomings of the method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1**: About modality translation and sentiment semantic reconstruction.\n\n**A1**: \n* Both modality translation and sentiment semantic reconstruction are designed for MSA tasks. Specifically, the core idea of modality translation is to utilize transitions among different modalities to achieve an effective extraction of sentiment-relevant representations. The purpose of the sentiment semantic reconstruction process is to ensure that the translation produces representations that can still contain sentiment semantics consistent with the original. \n* The design of translation loss and reconstruction loss is simple but effective, and the complex loss is difficult to train and performs poorly, the experimental results are shown in Table. 2 in **reply.pdf**. The testing conditions include Intra-Modality Missingness (Intra-MM), Inter-Modality Missingness (Inter-MM), and Complete Modality (CM). Obviously, the design in this paper has the best performance. They center on the form of supervision rather than the loss function.\n***\n\n**Q2**: About the technical contribution of HMI and HAL.\n\n**A2**: Thank you for your comments! We need to clarify that:\n* The multiscale feature-based knowledge distillation paradigm is indeed general and effective. However, the focus of this paper is not on the innovation of the knowledge distillation framework, but on fully utilizing the effective multiscale supervision paradigm in knowledge distillation to achieve hierarchical constraints between representations and missing feature reconstruction.\n* Distinguishing from the traditional simple constraint approach based on multi-scale features (\\emph{e.g.}, L2 distance), we propose a joint alignment mechanism based on mutual information maximization at the semantic level and adversarial learning at the distributional level. \n* Instead of simply employing HMI and HAL, we address the lack of effective alignment of semantics and distributions in the feature reconstruction process for existing MSA methods under the missing modality cases, as stated in lines 37-38 & 87-90 & 157-160 & 184-188 in the main manuscript. The ablation experiments in Section 4.4 in the main manuscript and Section A.3 in the Appendix demonstrate the superiority of the proposed method.\n***\n\n**Q3**: About the case studies and error analysis.\n\n**A3**: Valuable suggestions! \n* In order to better demonstrate the effectiveness of the proposed method, we used two challenging cases for case studies as shown in Fig. 3 in **reply.pdf**, where the underlined blue words may express emotional polarity and the missing modality is marked with a red dotted line.From the figure, we can find:  1) In E1, all models generate correct results though the visual modality is missing. Due to the strong guidance of the textual word  \"amazing\", the positive This case reveals that the conventional approaches can be well-performed when existing modalities express the same explicit semantics.  2) In E2, the textual modality expresses positive polarity, while the visual modality tends to be negative because of the frown and close lips on facial features. It is really hard to determine the polarity when the acoustic modality is missing. Specifically, Self-MM and CubeMLP misclassify the emotion as negative. Specifically, Self-MM and CubeMLP misclassify the emotion as negative, and the other approaches except ReFA all predict positive sentiment in terms of the dominance of the language modality. modality. In contrast, our framework recognizes correctly. This advantage stems from the present framework's factorization and capture of sentiment- relevant semantics, as well as the hierarchical knowledge distillation module's alignment of semantics and distributions, which accurately reconstructs missing features and produces robust joint multimodal representations. \n* We have conducted T-tests in Table. 1 and Table. 2 in the main manuscript, and the stable and highly significant experimental results demonstrate the superiority of the proposed method. Furthermore, we have described the limitations of our framework, as stated in the Section A.6 in the Appendix.\n***\n**Q4**: About the generalization of the framework using the teacher network.\n\n**A4**:  Valuable insights! \n* We have clarified this limitation, as stated in Section A.6 in the Appendix. In the future, we will strive to optimize the generality of the approach, e.g., by using the self-distillation paradigm in the framework. \n* The teacher network is trained on the original complete-modality data, which serves as a high-quality reference that transfers holistic knowledge contained in the complete samples to the student network. The student network accurately recovers the missing semantics during semantic and distributional alignment to the teacher network. \n* The teacher network covers the information of the complete modality, and this supervised paradigm is effective and generalizable in a variety of modality missing scenarios, thus enhancing the generalization of the student network.", "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": "67QK24AuTI", "reviewer": "Reviewer_f4LC", "summary": "The paper addresses the challenge of data incompleteness in Multimodal Sentiment Analysis (MSA). It introduces a novel approach called the Language-dominated Noise-resistant Learning Network (LNLN). The LNLN leverages the dense sentiment information in the language modality, considered the dominant modality, to improve robustness across various noise scenarios. It features two main components: a dominant modality correction (DMC) module and a dominant modality-based multimodal learning (DMML) module, which enhance the quality of the dominant modality representations. The model's performance was evaluated using datasets like MOSI, MOSEI, and SIMS, demonstrating superior robustness and accuracy compared to existing baselines. The comprehensive experiments provide new insights and a thorough comparative analysis in the context of incomplete data, advancing the field of MSA.", "questions": "Extending the approach to consider scenarios where visual or auditory data might be dominant could improve the versatility and applicability of the model.\n\nMore detailed ablation studies on MOSEI and SIMS would provide deeper insights into the workings and benefits of the proposed model.", "limitations": "yes", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "The introduction of the Language-dominated Noise-resistant Learning Network (LNLN) is innovative, addressing the issue of data incompleteness effectively by prioritizing the language modality, which is typically rich in sentiment information.\n\nThe authors conduct thorough experiments on well-known datasets (MOSI, MOSEI, SIMS), adding credibility to their findings. The detailed comparison with existing methods under diverse noise scenarios is particularly valuable.\n\nThe use of the Dominant Modality Correction (DMC) module and Dominant Modality Based Multimodal Learning (DMML) module is well-justified and systematically enhances the model’s robustness by ensuring the quality of dominant modality representations.", "weaknesses": "The focus on language as the dominant modality, while justified, may not generalize well to scenarios where other modalities (like visual or auditory) are equally or more critical. This could limit the applicability of the model to certain types of data or tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1**: About model applicability in different scenarios.\n\n**A1**: Many thanks to the reviewer for the constructive suggestions. We would like to clarify several points.\n(1) In the MSA task, the language modality contains more refined and rich sentiment semantics than the other modalities, and thus language plays a dominant role in MSA. (2) The training paradigm designed in this framework can effectively capture the complementary sentiment semantics among heterogeneous modalities, which enhances the applicability and scalability of the proposed model under multiple data types and multiple tasks. (3) Limited by rebuttal time, we promise to extend the proposed approach to other modality-dominated scenarios to provide comprehensive insights and perspectives in future work.\n***\n**Q2**: Add more detailed ablation studies.\n\n**A2**: Valuable Recommendations! The results of the ablation study of the proposed framework on the MOSEI and SIMS datasets are shown in Table. 3 in **reply.pdf**. We will add these ablation experiments to the revision. \n***", "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": "drvL7YBJha", "reviewer": "Reviewer_Zh5E", "summary": "The paper addresses the issue of missing modality information in multimodal systems and proposes solutions for two key problems:\n1. Excessively complex feature interactions lead to information redundancy and cumulative errors.\n2. Previous works did not align representations semantically.\n\nIn enhancing multimodal representations, the core approach utilizes the transformer’s encoder and decoder. It strengthens inter-modal interactions post-encoder to ensure semantic relevance is maintained in the decoder's reconstruction.\nThe model achieves state-of-the-art (SOTA) performance and includes detailed ablation studies and experimental analyses.", "questions": "Application Scenario: In the introductory example, if the crucial information \"bored\" is missing in L (Linguistic), A (Acoustic), and V (Visual) modalities, how is it still relevant to determine the task? What basis is used for this determination?\nModel Design: Why is it necessary to include a modality encoder in the model? Ensuring semantic interactions across different modalities should be sufficient.\nDid the baselines you compared against also use the SAME masking strategy as you did?", "limitations": "See Weaknesses and Questions.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 1, "contribution": 2, "strengths": "1. The model considers the differences between semantic and modality representations, utilizing the transformer’s encoder and decoder processes for reconstruction to facilitate inter-modal interactions.\n2. It accounts for the impact of Mutual Information (MI) on the learned representations, using MI to enhance the quality of these representations.\n3. The paper conducts thorough experiments, addressing the model's robustness when adjusting the modality missing ratio.", "weaknesses": "1. The use of numerous symbols in both the figures and text increases the complexity and time required for understanding. DIFFICULT TO READ\n2. In the introduction, the problem is not clearly stated; it is too brief. It only specifically mentions SMIL's approach without thoroughly analyzing the issues present in previous methods.\n3. It appears that the model relies heavily on MRM (Modality Stochastic Missing) for masking certain information to generate task-specific data. This raises concerns about the model's dependency on MRM. If MRM focuses on masking emotional words, does it hinder the model's understanding?\n4. The paper does not consider using large models to address this problem, nor does it compare the performance with that of large models.\n5. The HMI (Hierarchical Mutual Information) module applies Bengio's MI concepts rather straightforwardly, lacking in innovation.\n6. HAL (Hierarchical Adversarial Learning), presented as a separate contribution, does not make a significant impact in terms of performance or design. The hierarchical aspect merely reflects multi-scale representation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1**: About symbolic representation in figures and texts.\n\n**A1**: Thank you for the reminder! It is necessary that we use symbols in figures and text to represent the data flow and workflow of each component in the framework. We will improve the representation in the revision to make it easier to read.\n***\n**Q2**: About problems in the previous methods.\n\n**A2**: \n*  As stated in lines 34-38 in the main manuscript, we have summarized the common problems in previous methods: \n\n    * performing complex feature interactions on missing modalities leads to a large amount of information redundancy and cumulative errors, which affects the effectiveness of affective semantics extraction.\n\n    * The lack of consideration of semantic alignment and distributional alignment during feature reconstruction results in the inability to accurately recover features and produce robust joint multimodal representations. \n\n* In this paper, we only take SMIL as an example, which also suffers from the above issues and is not a special case. We promise to improve the representation in the revision.\n***\n**Q3**: If MRM focuses on masking emotional words, does it hinder the model's understanding?\n\n**A3**: Thank you for your comment! We need to clarify that:\n*  the MRM process is stochastic and does not mask out some specific words, and this stochastic paradigm effectively enhances the model's ability to deal with complex modality missing situations. \n* When MRM is discarded, the decreased performance of the model proves its indispensability and importance, as shown in Table. 4 in **reply.pdf**. The testing conditions include Intra-Modality Missingness (Intra-MM), Inter-Modality Missingness (Inter-MM), and Complete Modality (CM).\n***\n**Q4**: Please compare the proposed framework with the larger models.\n\n**A4**: Insightful comments. We want to emphasize the current immaturity of using large models to solve MSA tasks with three modalities: language, audio, and video. We did our best to select VideoLLaMA-1/2 that has good support for all three modalities in Table 5 of **reply.pdf** for comparison experiments.The testing conditions include Intra-Modality Missingness (Intra-MM), Inter-Modality Missingness (Inter-MM), and Complete Modality (CM). We find it difficult for large models to deliver significant gains when modality semantics are incomplete. In contrast, large models show promising potential for traditional perception tasks when all modalities are available.\n***\n**Q5**: Please describe the technical contributions of HMI and HAL.\n\n**A5**: We offer the following technical clarifications:\n* The concept of mutual information and adversarial learning are not simply combined and used, but are specifically designed to address the problems and limitations of existing MSA methods under uncertain missing modality.  Specifically, existing methods lack effective supervision of semantic alignment and distributional alignment during feature reconstruction, resulting in the inability to accurately restore missing sentiment semantics, as stated in lines 37-38 & 87-90 & 157-160 & 184-188 in the main manuscript. In contrast, the proposed interaction paradigm guides the reconstruction of missing sentiment information both at the semantic level and at the distributional level, thus recovering realistic modality features as much as possible. \n* HMI and HAL bring favorable performance gains, as shown in Table 3 in the main manuscript and Table 6 in the Appendix, which demonstrate the necessity and importance of both mechanisms.\n***\n**Q6**: Application Scenario: In the introductory example, if the crucial information \"bored\" is missing in L (Linguistic), A (Acoustic), and V (Visual) modalities, how is it still relevant to determine the task? What basis is used for this determination? Model Design: Why is it necessary to include a modality encoder in the model? Ensuring semantic interactions across different modalities should be sufficient. Did the baselines you compared against also use the SAME masking strategy as you did?\n\n**A6**: Thank you for your comments!\n\n* Application scenario: When critical information \"bored\" from all three modalities is missing, the model is still able to give judgments based on the following two grounds:\n\n1) The inherent imbalance in the label distribution in the MSA task (more positive than negative samples) leads the model to potentially rely on label bias as a statistical shortcut to perform predictions. Bias-driven predictions contain task-relevant a priori information to some extent [1].\n\n2) For multimodal sequential data with temporal asynchrony, MSA is often able to capture global contextual dependencies among elements during temporal modeling to provide task-relevant contextual semantics.\n\n* Model design: modal encoders are used to unify dimensions and provide refined modal representations for subsequent semantic interactions. For a fair comparison, the baseline and our framework use the same mask strategy.\n\n[1] Yang, Dingkang, et al. \"Towards multimodal sentiment analysis debiasing via bias purification.\" In ECCV 2024.", "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:
We thank all reviewers for their suggestions and thoughtful comments.

Meta-review: The paper studies the problem of incomplete multimodal sentiment analysis, where some modalities may be missing. The authors propose Representation Factorization and Alignment (ReFA) to tackle the issue of uncertain missing modalities in multimodal sentiment analysis (MSA). The proposed ReFA includes three components: Fine-grained Representation Factorization (FRF) module, Hierarchical Mutual Information (HMI) maximization mechanism, and Hierarchical Adversarial Learning (HAL) mechanism. Experiments are conducted on three datasets, demonstrating that the ReFA framework significantly improves MSA performance under both uncertain missing-modality and complete-modality testing conditions.

This paper received four reviews, including 2 strong accept, 1 accept, and 1 borderline accept. The reviewers recognized the novelty and contributions of this paper, such as practical issues, effective algorithms, comprehensive experiments, and good structure. 

Meanwhile, the reviewers pointed out some concerns and suggestions to further improve the quality of this paper, including but not limited to: lack of a detailed discussion on the computational complexity and runtime performance of the proposed framework, missing discussion of potential limitations, incremental technical contributions, lack of differentiation in handling the unique characteristics of each type of information, lack of relevant case studies, and too brief problem definition. The presentation of this paper also needs to be improved.

The authors provided meaningful responses to the reviewers' comments and concerns. I recommend acceptance of this paper and strongly recommend the authors take the reviewers' comments and the promised revisions into consideration when preparing the final version.
Decision: Accept (poster)