Multi-level Conflict-Aware Network for Multi-modal Sentiment Analysis

Haotian Wu; Lei Zhang; Yubo Gao

REVIEW 4 major objections 5 minor 30 references

Multi-level Conflict-Aware Network for Multi-modal Sentiment Analysis

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper claims that explicitly separating conflict from alignment in cross-modal representations improves multimodal sentiment analysis, and that this can be done without unstable generated labels by using singular value decomposition…

desk verdict A coherent conflict-aware fusion architecture with small consistent gains, but the SVD split's semantic story is not established by the evidence. read the letter →

arxiv 2502.09675 v1 pith:OLGN5SRS submitted 2025-02-13 cs.CL cs.AIcs.LG

Yubo Gao , Haotian Wu , Lei Zhang This is my paper · ORCID

classification cs.CLcs.AIcs.LG

keywords multimodalsentimentanalysisconflictmodelingsingularvaluedecompositioncross-modalattentionmulti-tasklearningCMU-MOSICMU-MOSEI

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that the interaction between modalities in sentiment analysis has two inseparable parts—agreement and conflict—and that prior methods mostly model agreement, missing conflicts that arise between pairs of modalities, such as a smiling face plus positive words while the voice is sarcastic. It proposes MCAN, a network that aligns text-audio and text-visual pairs, uses singular value decomposition to split each pair's representation into alignment and conflict parts, and then models the conflict parts with dedicated attention and discrepancy losses. The conflict modeling branch deliberately avoids generated unimodal labels used by earlier multi-task methods, relying on orthogonal representation constraints and prediction-difference losses instead. Experiments on the CMU-MOSI and CMU-MOSEI benchmarks report consistent gains over ten baselines, with the largest margins on 7-class accuracy and F1. If the result holds, conflict is a usable signal rather than noise, and it can be captured without labels that are themselves unreliable.

What carries the argument

The load-bearing mechanism is singular value decomposition (SVD) used as a spectral splitter. For a cross-modal feature matrix $F_{t,a} = U\Sigma V^\top$, the top-$k$ singular values and their singular vectors are reconstructed as the alignment constituent $F_{t,a}^{\text{aligned}}$, and the remaining singular values and vectors as the conflict constituent $F_{t,a}^{\text{conflict}}$; the paper selects $k=44$ by validation. This split is performed twice, first on unimodal pairs inside Micro-MSIN and then on bimodal pairs inside Macro-MSIN, so the network separates agreement from disagreement at two granularities. The separated conflict constituents are consumed by cross-attention modules that treat conflict as the query, and two discrepancy losses—orthogonality of features and distance between prediction heads—make the conflict branch expressive without relying on generated labels.

What would settle it

Train MCAN with a fixed random orthogonal projection of the same rank in place of the SVD split, keeping all other losses and hyperparameters identical. If accuracy on CMU-MOSI does not drop materially, the top-$k$ spectral directions are not the source of the gain. A complementary check: permute the pairing of audio and visual samples within each utterance so genuine alignment is destroyed while the spectral profile is unchanged; if the conflict branch still yields the same improvements, it is not detecting alignment versus conflict.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that conflict is a structured, learnable component of cross-modal representations, and it can be separated from alignment by rank truncation. MCAN treats text as the anchor modality, builds text-audio and text-visual representations through stacked cross-transformers (Micro-MSIN), and decomposes each concatenated representation with SVD: the top-$k$ singular directions form the alignment constituent and the rest form the conflict constituent. These conflict constituents are fused back into unimodal representations through conflict-aware cross-attention (Micro-CACA), while a second level (Macro-MSIN and Macro-CACA) repeats the same separation and fusion between the two bimodal representations. Two discrepancy losses—an orthogonal constraint on fused features and an $\ell^2$ difference between prediction heads—force the conflict branch to encode disagreement instead of collapsing into the main branch. The paper reports that this two-level separation improves over all compared baselines on both datasets.

Load-bearing premise

The load-bearing premise is that the largest singular values of the concatenated cross-modal feature matrix correspond to semantic alignment while the remaining ones correspond to conflict; if those top components mostly capture overall magnitude, scale, or dominant variance, the separation mechanism would not be doing the work the paper attributes to it.

Editorial extensions

If this is right

MCAN's two-level split implies that conflict exists not only between unimodal modalities but also between bimodal combinations, so models that only align unimodal pairs leave a measurable signal on the table.
Because the conflict branch is trained with discrepancy losses rather than generated labels, the same architecture could be applied in settings where unimodal labels are unreliable or unavailable.
The reported gains on CMU-MOSI and CMU-MOSEI indicate that the SVD truncation position is a sensitive hyperparameter: Table 2 shows Acc2 varying from 79.9 at top-8 to 84.3 at top-36, so the choice of $k$ is part of the method, not a free detail.
On the two benchmarks, the largest relative improvements are in 7-class accuracy and F1, suggesting the conflict branch helps most on fine-grained sentiment distinctions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The spectral assumption is not proven by the experiments: the top-$k$ directions of a concatenated feature matrix may capture shared variance or modality scale rather than semantic alignment, so the interpretation of the SVD split is a hypothesis the paper does not directly test.
A natural extension is to test whether the same two-level conflict separation transfers to other multimodal tasks such as sarcasm detection, emotion recognition, or multimodal hate speech, where disagreement between modalities is known to carry meaning.
The pairwise design leaves open a three-way conflict among all three modalities; a model that decomposes the full tri-modal tensor could capture conflicts that no single pair exhibits.
Because the discrepancy losses operate on features and predictions, the method could in principle be combined with gradient-based conflict modeling, which the paper itself names as future work.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a solid, workmanlike architecture paper. The new bit is applying SVD at two levels (unimodal and bimodal) to split cross-modal features into alignment and conflict constituents, then feeding the conflict parts to a branch trained with orthogonality and prediction-discrepancy losses. That combination is genuinely new as far as I know. The implementation is coherent, and the reported gains are consistent across CMU-MOSI and CMU-MOSEI (0.3 and 0.5 Acc2 over the strongest baseline, MMIN). The ablation is more thorough than many papers in this area—it tests each loss term, the branch itself, and the rank sweep.

Where it goes soft: the central claim that top-k singular values encode semantic alignment and the rest encode conflict is asserted, not demonstrated. Equation (4) is just a reconstruction split. The rank sweep in Table 2 shows Acc2 moving from 79.9 (k=8) to 84.5 (k=44), with non-monotonic behavior—exactly what you'd expect from a low-rank capacity knob or variance partition, not necessarily a semantic alignment/conflict division. The chosen k=44 is selected by validation, but no seeds or error bars are reported, so the 0.3–0.5 point gain over the best baseline is the same size as the swing from changing k by 8 (top-36 vs top-44 is 0.2). The phrase "significant improvement" in Section 3.2 is not backed by any statistical test. The paper also leaves some implementation details unspecified (e.g., how the SVD rank is chosen beyond "top-44", and whether that is on the validation set).

I don't think the method is unserious. The conflict branch with discrepancy constraints is a plausible regularizer, and it does appear to help. But the paper overstates how well we understand why it helps. The stress-test note is on target: the ablation evidence is as consistent with a capacity control story as with a semantic split. I also agree with the reader's conditional verdict—the architecture is novel enough to count, but the empirical backbone is too thin.

Who this is for: researchers in multimodal sentiment analysis and multimodal fusion. It doesn't open a new paradigm, but it adds a tool. I'd send it to review with the expectation that the authors provide error bars, a semantic control (e.g., showing the conflict constituent actually tracks contradiction or sarcasm annotations), and either justify the rank selection or make it adaptive/regularized.

Recommendation: accept for peer review, conditional on those additions.

Referee Report

4 major / 5 minor

Summary. The paper proposes MCAN (Multi-level Conflict-Aware Network) for multimodal sentiment analysis. The main branch extracts language, acoustic, and visual features, applies cross-modal transformers to produce pairwise features (e.g., Ft,a, Ft,v), and then uses SVD to split each pairwise feature into top-k 'alignment' and residual 'conflict' constituents. The aligned constituents feed a Macro-MSIN, while the conflict constituents are processed by a separate conflict modeling branch using micro and macro conflict-aware cross-attention, with orthogonality and prediction-difference losses. Experiments on CMU-MOSI and CMU-MOSEI report consistent but small improvements over ten baselines (e.g., Acc2 84.5 vs. 84.2 on CMU-MOSI and 85.8 vs. 85.3 on CMU-MOSEI), plus ablations over loss components, the conflict branch, and SVD truncation rank.

Significance. If the proposed mechanism is validated, explicitly segregating alignment and conflict at both unimodal and bimodal levels is a plausible and interesting direction for MSA, and avoiding generated unimodal labels is practically attractive. The paper is commendably explicit about its loss terms and includes an ablation over the SVD truncation rank and the main loss components. However, the current evidence is not conclusive: the output-level discrepancy loss is written as a minimized squared difference, which encourages agreement rather than conflict; no error bars or significance tests support the claimed 'significant improvement'; and the semantic interpretation of the SVD split is not distinguished from a low-rank capacity or variance partition. These issues bear directly on the paper's central claim, so the manuscript needs substantial revision before the results can be accepted as stated.

major comments (4)

[Sec. 2.2.1, Eq. (8); Sec. 2.2.2, Eq. (9); Sec. 2.1, Eq. (10)] The text states that the conflict modeling branch encourages unimodal representations 'to generate distinct predictions as much as possible' (Sec. 2.2.1), but Ldiff_micro and Ldiff_macro are sums of squared differences between prediction outputs, and these terms enter the total loss with positive weights in Eq. (10). Minimizing L therefore minimizes |ŷ'_p − ŷ'_q|^2 and |ŷ''_{t,v} − ŷ''_{t,a}|^2, driving predictions toward agreement rather than disagreement. As written, the output-level term is a consistency regularizer, not a conflict/discrepancy regularizer. Please correct the sign or the optimization direction (e.g., maximize a bounded discrepancy or use gradient reversal) and rerun the experiments; this is a load-bearing part of the proposed method.
[Sec. 3.2, Table 1] The claim that MCAN 'achieves significant improvement compared to baselines' is not supported by the reported evidence. No standard deviations, number of random seeds, or significance tests are provided, and the margins over the strongest baseline MMIN are only 0.3/0.5 Acc2 on CMU-MOSI/CMU-MOSEI. In contrast, the ablation study in Table 2 shows that the SVD truncation rank alone changes Acc2 from 79.9 to 84.5, a swing much larger than the claimed gain. Please report mean and standard deviation over multiple seeds and a formal significance test, and avoid the word 'significant' without such support.
[Sec. 2.1.1, Eq. (4); Sec. 3.3, Table 2] The core mechanism assumes that the top-k singular values of each cross-modal feature matrix encode semantic alignment while the remaining singular values encode conflict, but no evidence for this semantic interpretation is provided. The rank sweep in Table 2 is equally consistent with the SVD split acting as a low-rank capacity or variance partition (e.g., top-8 gives 79.9, top-16 gives 83.8, top-36 gives 84.3, top-52 gives 83.4, top-64 gives 83.0). Please add a semantic control or analysis, such as inspecting what the alignment and conflict constituents represent on examples, comparing with a random fixed spectral mask of the same rank, or showing that the conflict branch carries information orthogonal to the main branch. Also specify how k=44 was selected (e.g., on a validation split) and report the selection procedure.
[Sec. 3.1 and Sec. 3.3] The implementation details are too sparse for reproducibility: the manuscript does not report the number of Micro-MSIN/Macro-MSIN layers, number of attention heads, hidden dimension, batch size, training epochs, learning-rate schedule, or data splits. The weights α and β are fixed at 1e-2 and 1e-3 without any sensitivity analysis. Given the nonstandard SVD-based split and the discrepancy losses, these details matter for assessing whether the reported gains are stable and not an artifact of a particular configuration.

minor comments (5)

[Sec. 3.2 and Sec. 3.3] Table references are inconsistent: Sec. 3.2 refers to 'Table 2.2.2' instead of Table 1, and Sec. 3.3 refers to 'Table 3.2' for the ablation table. The text in Sec. 3.3 also contains typographical errors such as 'Ldif fand Loc' and 'Ldif for Loc'.
[Eq. (4)] The notation in Eq. (4) should be clarified: h is used both for the rank of Σ and for the number of retained singular values, and the relationship between h and the matrix dimensions m and n is not stated. Please define the dimensions of the reconstructed alignment and conflict constituents explicitly.
[Eq. (7) and Eq. (8)] The notation in Eq. (7), such as the Frobenius norm of F'^T_p F'_q, is rendered poorly and should be defined. Similarly, the prediction variables ŷ'_p and ŷ''_{t,v} should be explicitly defined after the FFN prediction heads are introduced.
[Eq. (6)] In Micro-CACA, the query is derived from F_conflict_{t,v} while the keys and values come from Ft; please state how the feature dimensions of the conflict constituent and the modality feature are made compatible before the attention computation.
[Conclusion] The conclusion states that MCAN 'outperforms the current state-of-the-art methods'; since Table 1 compares only a fixed set of baselines, please soften this claim to 'the baselines considered in this paper' unless a broader SOTA comparison is provided.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: MCAN's claims are empirical benchmark results, and the SVD split and discrepancy losses are evaluated on held-out CMU-MOSI and CMU-MOSEI data rather than on their own training objectives.

full rationale

This paper contains no derivation chain that reduces to its own inputs. The central claim is empirical: MCAN achieves Acc2 84.5 on CMU-MOSI and 85.8 on CMU-MOSEI, compared with baselines, as reported in Table 1. These numbers come from held-out benchmark evaluation, not from the training losses defined in Eq. (10). The conflict modeling branch is trained with discrepancy losses (Eqs. 7-9), but its contribution is measured by final task accuracy on the test sets, an external criterion independent of those losses. The SVD truncation in Eq. (4) is a hand-set hyperparameter (top-44, cited in Section 3.1) selected by validation performance; this is hyperparameter tuning, not a fitted input renamed as a prediction. The ablation in Section 3.3 shows sensitivity to the truncation position, which is evidence of model bias but not circularity. There are no self-citations that carry the argument: references [16,17] motivate SVD-based spectral operations but are external prior works, and no uniqueness theorem or prior result by these authors is invoked to force the architecture. The claimed alignment/conflict semantics of the SVD split are assumptions about spectral properties, and a skeptical reader might question whether the split is semantically meaningful; however, under the stated rules, that is a correctness or robustness concern, not a circularity concern. The paper is self-contained against external benchmarks, so the honest finding is no significant circularity with score 0.

Assumptions & free parameters 3 free parameters · 5 assumptions · 2 invented entities

The central claim rests on two spectral latent constructs (alignment and conflict constituents), one tuned SVD rank, two loss weights, and the domain assumptions listed above. The SVD rank and loss weights are fit to validation behavior, which is a fitting risk; the semantic interpretation of singular vectors is an unverified assumption.

free parameters (3)

SVD truncation rank k = 44 (for final model; ablation tested 8 to 64)
The split between alignment and conflict constituents is set by the number of retained singular values; the authors select top-44 and show performance varies from Acc2 79.9 to 84.3 across k, so this choice substantially influences results.
alpha (orthogonality loss weight) = 1e-2
Trade-off weight for representation-level discrepancy constraints in Eq. (10); chosen on validation data.
beta (prediction discrepancy loss weight) = 1e-3
Trade-off weight for output-level discrepancy constraints in Eq. (10); chosen on validation data.

assumptions (5)

domain assumption The largest singular values of a concatenated cross-modal feature matrix correspond to semantically aligned information, while the remainder corresponds to conflict.
Invoked in Section 2.1.1 around Eq. (4); no evidence ties spectral magnitude to alignment versus conflict semantics.
domain assumption Text is the dominant modality, so no direct audio-visual Micro-MSIN interaction is needed.
Stated in Section 2.1.1: the authors do not set Micro-MSIN between audio and visual features because text is treated as the main contributing modality. If audio-visual conflicts are informative, this design misses them.
domain assumption Encouraging orthogonal representation features and divergent prediction heads captures conflict constituents.
The conflict branch is trained with orthogonality and prediction-difference losses (Eqs. 7-9); this is an optimization prior, not a demonstrated correspondence to human-perceived conflict.
domain assumption The CMU-MOSI and CMU-MOSEI labels and evaluation metrics are reliable and the baseline results are comparable.
The paper takes standard benchmark and baseline numbers at face value without cross-checking runs or reporting variance.
standard math Singular value decomposition and low-rank reconstruction are well-defined mathematical operations.
The paper relies on the existence and reconstruction properties of SVD in Eq. (4); this is standard background, not a weakness.

invented entities (2)

Alignment constituent (F^aligned)
purpose: Low-rank SVD reconstruction of cross-modal features that the model treats as consistent information between modalities.
Defined by top-k singular values in Eq. (4); no external behavioral or human-judgment evidence that this subspace equals alignment.
Conflict constituent (F^conflict)
purpose: Residual SVD reconstruction that the conflict modeling branch consumes to capture inter-modal disagreement.
Defined as the complement of the alignment subspace; whether it captures sarcasm-like conflict is assessed only through downstream sentiment accuracy.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Multi-level Conflict-Aware Network for Multi-modal Sentiment Analysis." pith.science (2026). https://pith.science/paper/OLGN5SRS

@misc{pith2026250209675,
  author       = {Pith},
  title        = {Pith review of: Multi-level Conflict-Aware Network for Multi-modal Sentiment Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OLGN5SRS}},
  note         = {Machine review of arXiv:2502.09675}
}

read the original abstract

Multimodal Sentiment Analysis (MSA) aims to recognize human emotions by exploiting textual, acoustic, and visual modalities, and thus how to make full use of the interactions between different modalities is a central challenge of MSA. Interaction contains alignment and conflict aspects. Current works mainly emphasize alignment and the inherent differences between unimodal modalities, neglecting the fact that there are also potential conflicts between bimodal combinations. Additionally, multi-task learning-based conflict modeling methods often rely on the unstable generated labels. To address these challenges, we propose a novel multi-level conflict-aware network (MCAN) for multimodal sentiment analysis, which progressively segregates alignment and conflict constituents from unimodal and bimodal representations, and further exploits the conflict constituents with the conflict modeling branch. In the conflict modeling branch, we conduct discrepancy constraints at both the representation and predicted output levels, avoiding dependence on the generated labels. Experimental results on the CMU-MOSI and CMU-MOSEI datasets demonstrate the effectiveness of the proposed MCAN.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

30 extracted references · 27 canonical work pages

[1]

INTRODUCTION In recent years, multimodal sentiment analysis (MSA) has attracted increasingly widespread attention [1, 2, 3, 4]. Be- cause of the heterogeneity among multimodal data, how to effectively fuse the representations of different modalities and ensure the semantic integrity of modalities is an impor- tant research topic in the community of MSA [5...

work page Pith review arXiv 2025
[2]

MCAN first con- ducts feature extraction for the three input modalities

METHODOLOGY The framework of the proposed multi-level conflict-aware network (MCAN) is shown in Figure 2. MCAN first con- ducts feature extraction for the three input modalities. For language modality, we feed the input text into BERT to ob- tain the language feature Ft. While LSTM is adopted to capture the intra-modality interaction Fv and Fa for visual ...

work page
[3]

Datasets, Metrics and Implementation Details We evaluate MCAN on CMU-MOSI [22] and CMU-MOSEI

EXPERIMENT 3.1. Datasets, Metrics and Implementation Details We evaluate MCAN on CMU-MOSI [22] and CMU-MOSEI

work page
[4]

CONLUSION In this paper, we develop a novel MCAN for MSA. To balance the discrepancies between unimodal and bimodal representations while fusing and aligning cross-modal representations, MCAN is divided into a main branch and a conflict modeling branch, which are jointly trained in a multi-task learning manner. The former progressively extracts different ...

work page
[5]

ACKNOWLEDGE The work was supported by the National Natural Science Foundation of China (No.72271017)

work page
[6]

Ef- ficient low-rank multimodal fusion with modality-specific fac- tors,

Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, Paul Pu Liang, Amir Zadeh, and Louis-Philippe Morency, “Ef- ficient low-rank multimodal fusion with modality-specific fac- tors,” arXiv preprint arXiv:1806.00064, 2018

arXiv 2018
[7]

Tensor fusion network for mul- timodal sentiment analysis,

Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency, “Tensor fusion network for mul- timodal sentiment analysis,” in Proceedings of the 2017 Con- ference on Empirical Methods in Natural Language Process- ing, 2017, pp. 1103–1114

work page 2017
[8]

Multimodal transformer for unaligned multimodal language sequences,

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. NIH Public Access, 2019, vol. 2019, p. 6558

work page 2019

Show all 30 references

[9]

Misa: Modality-invariant and-specific representations for multimodal sentiment analysis,

Devamanyu Hazarika, Roger Zimmermann, and Soujanya Po- ria, “Misa: Modality-invariant and-specific representations for multimodal sentiment analysis,” in Proceedings of the 28th ACM international conference on multimedia, 2020, pp. 1122– 1131

2020
[10]

Multi-grained multimodal interaction network for sentiment analysis,

Lingyong Fang, Gongshen Liu, and Ru Zhang, “Multi-grained multimodal interaction network for sentiment analysis,” in ICASSP 2024-2024 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP). IEEE, 2024, pp. 7730–7734

2024
[11]

Cross-modal enhancement network for multi- modal sentiment analysis,

Di Wang, Shuai Liu, Quan Wang, Yumin Tian, Lihuo He, and Xinbo Gao, “Cross-modal enhancement network for multi- modal sentiment analysis,” IEEE Transactions on Multimedia, vol. 25, pp. 4909–4921, 2022

2022
[12]

Decoupled multi- modal distilling for emotion recognition,

Yong Li, Yuanzhi Wang, and Zhen Cui, “Decoupled multi- modal distilling for emotion recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 6631–6640

2023
[13]

Multi-attention recur- rent network for human communication comprehension,

Amir Zadeh, Paul Pu Liang, Soujanya Poria, Prateek Vij, Erik Cambria, and Louis-Philippe Morency, “Multi-attention recur- rent network for human communication comprehension,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2018, vol. 32

2018
[14]

Attention is all you need,

A Vaswani, “Attention is all you need,” Advances in Neural Information Processing Systems, 2017

2017
[15]

Tetfn: A text enhanced transformer fusion net- work for multimodal sentiment analysis,

Di Wang, Xutong Guo, Yumin Tian, Jinhui Liu, LiHuo He, and Xuemei Luo, “Tetfn: A text enhanced transformer fusion net- work for multimodal sentiment analysis,”Pattern Recognition, vol. 136, pp. 109259, 2023

2023
[16]

Multimodal sentiment analysis based on 3d stereoscopic attention,

Jian Huang, Yuanyuan Pu, Dongming Zhou, Hang Shi, Zheng- peng Zhao, Dan Xu, and Jinde Cao, “Multimodal sentiment analysis based on 3d stereoscopic attention,” in ICASSP 2024- 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024, pp...

2024
[17]

Disentangled representation learning for multimodal emotion recognition,

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, 2022, pp. 1642–1651

2022
[18]

Large language models meet text-centric multimodal sentiment anal- ysis: A survey,

Hao Yang, Yanyan Zhao, Yang Wu, Shilong Wang, Tian Zheng, Hongbo Zhang, Wanxiang Che, and Bing Qin, “Large language models meet text-centric multimodal sentiment anal- ysis: A survey,” arXiv preprint arXiv:2406.08068, 2024

2024 arXiv
[19]

Learning modality-specific representations with self-supervised multi- task learning for multimodal sentiment analysis,

Wenmeng Yu, Hua Xu, Ziqi Yuan, and Jiele Wu, “Learning modality-specific representations with self-supervised multi- task learning for multimodal sentiment analysis,” in Proceed- ings of the AAAI conference on artificial intelligence , 2021, vol. 35, pp. 10790–10797

2021
[20]

A novel multimodal sentiment analysis model based on gated fusion and multi-task learning,

Xin Sun, Xiangyu Ren, and Xiaohao Xie, “A novel multimodal sentiment analysis model based on gated fusion and multi-task learning,” in ICASSP 2024-2024 IEEE International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 8336–8340

2024
[21]

Ch-sims: A chi- nese multimodal sentiment analysis dataset with fine-grained annotation of modality,

Wenmeng Yu, Hua Xu, Fanyang Meng, Yilin Zhu, Yixiao Ma, Jiele Wu, Jiyun Zou, and Kaicheng Yang, “Ch-sims: A chi- nese multimodal sentiment analysis dataset with fine-grained annotation of modality,” in Proceedings of the 58th annual meeting of the association for computational...

2020
[22]

Transferability vs. discriminability: Batch spectral pe- nalization for adversarial domain adaptation,

Xinyang Chen, Sinan Wang, Mingsheng Long, and Jianmin Wang, “Transferability vs. discriminability: Batch spectral pe- nalization for adversarial domain adaptation,” in International conference on machine learning. PMLR, 2019, pp. 1081–1090

2019
[23]

datasets, which are the most widely used benchmark for MSA. Five different metrics are employed to evaluate the per- formance of MCAN and baselines: binary accuracy (Acc2), 7-class accuracy (Acc7), F1 Score (F1), Pearson correlation (Corr), and mean absolute error (MAE). For t...
[24]

Gener- alized and discriminative few-shot object detection via svd- dictionary enhancement,

Aming Wu, Suqi Zhao, Cheng Deng, and Wei Liu, “Gener- alized and discriminative few-shot object detection via svd- dictionary enhancement,” Advances in Neural Information Processing Systems, vol. 34, pp. 6353–6364, 2021

2021
[25]

Multi-task momentum distil- lation for multimodal sentiment analysis,

Ronghao Lin and Haifeng Hu, “Multi-task momentum distil- lation for multimodal sentiment analysis,” IEEE Transactions on Affective Computing, 2023

2023
[26]

Learning language-guided adaptive hyper-modality representation for multimodal sentiment anal- ysis,

Haoyu Zhang, Yu Wang, Guanghao Yin, Kejun Liu, Yuanyuan Liu, and Tianshu Yu, “Learning language-guided adaptive hyper-modality representation for multimodal sentiment anal- ysis,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023, ...

2023
[27]

Cross- modality representation interactive learning for multimodal sentiment analysis,

Jian Huang, Yanli Ji, Yang Yang, and Heng Tao Shen, “Cross- modality representation interactive learning for multimodal sentiment analysis,” in Proceedings of the 31st ACM Inter- national Conference on Multimedia, 2023, pp. 426–434

2023
[28]

Multimodal sentiment intensity analysis in videos: Facial gestures and verbal messages,

Amir Zadeh, Rowan Zellers, Eli Pincus, and Louis-Philippe Morency, “Multimodal sentiment intensity analysis in videos: Facial gestures and verbal messages,” IEEE Intelligent Sys- tems, vol. 31, no. 6, pp. 82–88, 2016

2016
[29]

Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dy- namic fusion graph,

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 dy- namic fusion graph,” in Proceedings of the 56th Annual Meet- ing of the Association for Computation...

2018
[30]

Words can shift: Dy- namically adjusting word representations using nonverbal be- haviors,

Yansen Wang, Ying Shen, Zhun Liu, Paul Pu Liang, Amir Zadeh, and Louis-Philippe Morency, “Words can shift: Dy- namically adjusting word representations using nonverbal be- haviors,” in Proceedings of the AAAI conference on artificial intelligence, 2019, vol. 33, pp. 7216–7223

2019

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

INTRODUCTION In recent years, multimodal sentiment analysis (MSA) has attracted increasingly widespread attention [1, 2, 3, 4]. Be- cause of the heterogeneity among multimodal data, how to effectively fuse the representations of different modalities and ensure the semantic integrity of modalities is an impor- tant research topic in the community of MSA [5...

work page Pith review arXiv 2025

[2] [2]

MCAN first con- ducts feature extraction for the three input modalities

METHODOLOGY The framework of the proposed multi-level conflict-aware network (MCAN) is shown in Figure 2. MCAN first con- ducts feature extraction for the three input modalities. For language modality, we feed the input text into BERT to ob- tain the language feature Ft. While LSTM is adopted to capture the intra-modality interaction Fv and Fa for visual ...

work page

[3] [3]

Datasets, Metrics and Implementation Details We evaluate MCAN on CMU-MOSI [22] and CMU-MOSEI

EXPERIMENT 3.1. Datasets, Metrics and Implementation Details We evaluate MCAN on CMU-MOSI [22] and CMU-MOSEI

work page

[4] [4]

CONLUSION In this paper, we develop a novel MCAN for MSA. To balance the discrepancies between unimodal and bimodal representations while fusing and aligning cross-modal representations, MCAN is divided into a main branch and a conflict modeling branch, which are jointly trained in a multi-task learning manner. The former progressively extracts different ...

work page

[5] [5]

ACKNOWLEDGE The work was supported by the National Natural Science Foundation of China (No.72271017)

work page

[6] [6]

Ef- ficient low-rank multimodal fusion with modality-specific fac- tors,

Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, Paul Pu Liang, Amir Zadeh, and Louis-Philippe Morency, “Ef- ficient low-rank multimodal fusion with modality-specific fac- tors,” arXiv preprint arXiv:1806.00064, 2018

arXiv 2018

[7] [7]

Tensor fusion network for mul- timodal sentiment analysis,

Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency, “Tensor fusion network for mul- timodal sentiment analysis,” in Proceedings of the 2017 Con- ference on Empirical Methods in Natural Language Process- ing, 2017, pp. 1103–1114

work page 2017

[8] [8]

Multimodal transformer for unaligned multimodal language sequences,

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. NIH Public Access, 2019, vol. 2019, p. 6558

work page 2019