Modality Agreement- and Conflict-Aware Prototype Hypergraph Learning for Multimodal Intent Understanding

Ayan Dutta; Chandranath Adak; Mohnish Raj; Soumi Chattopadhayay; Suraj Kumar

REVIEW 3 major objections 7 minor 24 references

Modality Agreement- and Conflict-Aware Prototype Hypergraph Learning for Multimodal Intent Understanding

T0 review · 3 major / 7 minor · reviewed 2026-08-08 · deepseek-v4-flash

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

Pith's one-line read MACH is a hierarchical prototype-hpergraph framework that treats modality agreement and conflict as separate reusable structures and, on three benchmarks, reports the best accuracy and F1 among all compared multimodal intent recognition…

desk verdict Coherent and genuinely new architecture for modeling agreement and conflict as structured patterns; the 'consistently best' claim overreaches on MELD-DA, and the baseline comparison needs a backbone check, but the core idea and ablations deserve review. read the letter →

arxiv 2608.04054 v1 pith:ZMC5RZPM submitted 2026-08-04 cs.MM cs.AIcs.CLcs.LG

Mohnish Raj , Suraj Kumar , Soumi Chattopadhayay , Chandranath Adak , Ayan Dutta This is my paper · ORCID

classification cs.MMcs.AIcs.CLcs.LG

keywords multimodalintentrecognitionhypergraphlearningprototypemodalityagreementconflicthierarchicalcompositioncross-modalfusionsarcasmdetection

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 argues that multimodal intent recognition should treat agreement and conflict between text, audio, and video as reusable structured patterns, not as by-products of fusion. It introduces MACH, a hierarchical framework that builds separate prototype hypergraphs for agreement and conflict at unimodal, bimodal, and trimodal levels, then adaptively blends them per sample. On three benchmarks (MIntRec, MIntRec2.0, MELD-DA), MACH reports the best accuracy and F1 across all compared baselines, and ablations show that removing either pathway or the prototypes lowers performance. If the results hold, the work suggests that explicit, reusable disagreement modeling adds signal over alignment-only or uncertainty-weighted fusion.

What carries the argument

The central object is the prototype hypergraph. Each hyperedge connects an interaction anchor (a unimodal, bimodal, or trimodal composition) to its top-r nearest prototypes from a per-unit prototype bank; normalized HGNN propagation then lets the anchor borrow consensus structure from semantically related anchors while keeping the graph sparse. Conflict is a separate pipeline: after agreement propagation, pairwise absolute differences $c^{(\ell)} = \rho^{(\ell)}\left(\left[\,|u^{(\ell)}_{s_1}-u^{(\ell)}_{s_2}|\,\right]_{s_1<s_2}\right)$ are mapped to conflict prototypes; arbitration $\omega^{(\ell)} = \sigma(W^{(\ell)}_\omega [A^{(\ell)}; C^{(\ell)}] + b^{(\ell)}_\omega)$ blends the two feature-wise. The hierarchy $I^{(0)}\to I^{(1)}\to I^{(2)}$ carries refined anchors upward so higher levels reuse lower-level agreement.

What would settle it

Train a variant in which conflict is computed from the raw modality projections before agreement propagation; if accuracy on MIntRec2.0 does not drop below 65.67%, the specific Eq. (6) formulation is not carrying the argument. Separately, give the strongest baselines the same Qwen2.5-Omni-7B backbone; if the gap closes, the reported margin comes from the encoder rather than the architecture.

Watch

Extended reading notes

Core claim

MACH's central claim is that communicative intent is better predicted by two complementary interaction spaces—an agreement space capturing what modalities share and a conflict space capturing how they differ—than by a single fused representation. The framework composes unimodal anchors into bimodal and trimodal anchors, refines each anchor through sparse prototype hypergraphs that connect it to its top-r nearest reusable prototypes, and computes conflict as pairwise absolute differences between the refined anchors, passed through dedicated conflict prototypes. A feature-wise arbitration gate weights agreement vs conflict for each sample, and a six-phase curriculum stabilizes the hierarchy level by level. The paper reports that this configuration reaches 80.99% accuracy on MIntRec, 65.67% on MIntRec2.0, and 62.11% on MELD-DA, ahead of all compared fusion, confidence-aware, and hypergraph baselines, and that the ablation results attribute the gain to the agreement and conflict branches.

Load-bearing premise

The model's conflict pathway can only see disagreement that survives as a pairwise absolute difference between agreement-refined anchors, so any informative conflict that does not show up as such a residual is invisible to it.

Editorial extensions

If this is right

If MACH is right, multimodal intent systems gain by keeping disagreement explicit rather than aligning or down-weighting it.
Reusable prototype hypergraphs give a mechanism for cross-utterance transfer: similar interaction patterns, not just similar content, become shared evidence.
The hierarchical curriculum implies that joint training of agreement and conflict benefits from first stabilizing the agreement hierarchy, a scheduling choice that other multimodal architectures could adopt.
The method extends to a third benchmark (emotion recognition), suggesting the agreement-conflict decomposition transfers beyond intent to other multimodal language tasks.

Reading between the lines

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

A natural test the paper leaves implicit is whether the conflict pathway captures anything beyond what a supervised contrastive loss already does: the ablation shows removing the conflict branch hurts less than removing agreement, so conflict may be a refinement on top of consensus.
One could probe Eq. (6)'s assumption directly by training a variant where conflict is computed before agreement propagation; if that variant matches MACH, the absolute-difference-on-refined-anchors formulation is not itself the critical step.
The reported backbone difference (Qwen2.5-Omni-7B + QLoRA for MACH only) suggests a controlled re-baselining experiment: giving the strongest baselines the same backbone would show how much of the margin is architectural.
Because visual-only input performs near chance (~16% on MIntRec), the visual modality's contribution in MACH appears to be mainly through its agreement/conflict relationship with text and audio—an observation worth isolating in a modality-pair study.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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

Short version: this is a solid architecture paper with a genuinely new integration—separate agreement and conflict prototype hypergraphs nested inside a progressive modality-composition hierarchy—and the ablations are thorough. The main claim in Section IV-A, 'consistently achieves the best performance across all reported metrics,' is too strong: on MELD-DA macro F1, HIER (54.80) beats MACH (52.81). That's a factual overstatement, not a fatal flaw. The bigger issue is whether Table II's comparisons are fair. Only MACH is described as using Qwen2.5-Omni-7B with QLoRA; the baselines' backbones are never stated. If those rows come from the original papers with BERT/RoBERTa-style features, the reported margins could be largely encoder choice rather than the agreement/conflict hypergraphs. The authors do include a 'Qwen2.5-Omni-7B alone' row in Table III (77.98 Acc on MIntRec) that sits well below MACH's 80.99, so the encoder alone is not sufficient. But that still does not tell us how HIER or HyperModal would fare with the same 7B backbone. This is load-bearing and needs an explicit answer.

What is actually new: prior work used prototypes for class discrimination and hypergraphs for feature propagation; here they are used to store reusable agreement and conflict interaction patterns at each composition level, with a sample-adaptive arbitration between the two pathways. The hierarchy is progressive (unimodal to bimodal to trimodal) rather than a single fusion step. The architecture is coherent, the four objectives are reasonable, and the ablations in Tables III and IV consistently show each component contributing, with small standard deviations over five runs. The conflict-as-residual definition in Eq. (6) is a deliberate simplification—informative disagreement that is not an L1 residual after agreement propagation is invisible to the model. That is a scope limitation, not an error, and the paper is honest about constructing conflict that way.

Who this is for: anyone working on multimodal fusion, sarcasm or taunting detection, or intent recognition who wants a concrete way to model disagreement as structure rather than noise. It deserves a serious referee. Ask the authors to (1) re-run or clearly report baselines with the same backbone, or at least ablate the backbone for the top baselines, and (2) correct the 'consistently best' sentence. With those changes, the core contribution stands.

Referee Report

3 major / 7 minor

Summary. The paper proposes MACH, a hierarchical prototype-hypergraph framework for multimodal intent understanding. MACH decomposes cross-modal interaction into agreement and conflict pathways: unimodal features are projected to anchors, composed into bimodal and trimodal anchors, refined by sparse prototype hypergraphs, and combined through feature-wise adaptive arbitration. A six-phase progressive curriculum and four auxiliary losses (hierarchical InfoNCE, prototype regularization, supervised contrastive, and classification) are used for training. Experiments on MIntRec, MIntRec2.0, and MELD-DA report accuracy and F1 improvements, with component, loss, and modality ablations. The paper claims consistent state-of-the-art performance across all reported metrics.

Significance. The central idea—treating multimodal agreement and conflict as reusable, structured interaction patterns rather than as by-products of fusion—is timely and potentially valuable for multimodal intent recognition. The architecture is comprehensive, the ablations are logically organized, five-run standard deviations are reported, and a supplementary repository is provided. These are genuine strengths. However, the state-of-the-art claim is not currently supported because the main comparison is not controlled for the feature encoder, and one headline claim is contradicted by the paper's own Table II. If the authors re-run baselines on the same backbone and correct the overstatements, the contribution would be credible.

major comments (3)

[Section IV-A/Table II] The main comparative claim is confounded by the feature encoder. The experimental settings state that Qwen2.5-Omni-7B was adapted with QLoRA for MACH, but no sentence states that the Table II baselines were re-run with this same backbone. Table III's frozen-encoder row shows that Qwen2.5-Omni-7B alone reaches 64.88 Accuracy on MIntRec2.0, which is higher than HyperGCL (63.01) and HyperModal (64.28) in Table II; on that dataset the encoder alone accounts for most of the reported margin. The central claim that the agreement/conflict prototype hypergraphs, not the 7B omni-modal encoder, drive the gains therefore requires either re-running all baselines on the same backbone or adding controlled variants such as the Qwen backbone combined with existing fusion heads.
[Section IV-A/Table II] The sentence 'MACH consistently achieves the best performance across all reported metrics' is contradicted by Table II: on MELD-DA macro F1, HIER achieves 54.80 while MACH achieves 52.81. Even among the other MELD-DA metrics, the gains over HIER are small (Accuracy 62.11 vs. 61.95, WF1 60.85 vs. 60.38), while the macro-F1 loss is 1.99 points. The claim should be restricted to MIntRec and MIntRec2.0, or to the specific metrics where MACH actually leads.
[Section III-F/Eq. (6)] Conflict is defined as pairwise absolute differences between updated agreement anchors after agreement propagation. This makes the conflict signal a residual of the agreement branch by construction: any disagreement that is not expressible as an L1 residual magnitude after hypergraph propagation is structurally invisible to the conflict pathway, and the conflict space C is not truly independent of A. Since the paper's central claim is that informative disagreement deserves a dedicated structured representation, the authors should justify this residual definition, test a non-residual alternative (for example, direct pairwise differences of the raw anchors, or signed differences), and discuss which kinds of disagreement are out of scope.

minor comments (7)

[Table II] The HIER row contains '60.3854.80' without a delimiter between WF1 and F1; please fix the formatting.
[Section IV-C/Table IV] The text numbers do not all match the table: on MIntRec2.0, removing the hierarchical loss reduces Accuracy by 2.94 (not 2.91) and WF1 by 2.61 (not 2.85); on MIntRec, removing the diversity loss reduces WF1 by 3.66 (not 3.63). Please reconcile the prose with Table IV.
[Section IV-B/Table III] The 'Only Visual' accuracy is 16.40 on MIntRec and 13.85 on MIntRec2.0, far below the other unimodal settings; the paper should comment on whether the frozen encoder's visual features are adequate, since this affects how the audiovisual-composition results are interpreted.
[Section III-I/Eq. (9)] The negative set for the InfoNCE objective is described only as 'samples from other instances in the mini-batch'; please specify whether negatives are drawn per interaction unit and whether any hard-negative mining is used.
[Section III-F/Eq. (6)] The notation [|u_{s1} - u_{s2}|] is ambiguous about whether the absolute difference is applied element-wise and how the pairwise features are concatenated before the network rho; please clarify.
[Section IV-A] MELD-DA is an emotion-recognition dataset, not an intent-recognition dataset; the phrase about generalizing to 'diverse multimodal intent recognition scenarios' should be adjusted to avoid overclaiming.
[Section III] The main text does not state default values for K, r, K_c, r_c, tau, or the lambda weights in Eq. (11); please give them in the main text or point precisely to the relevant supplementary section.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: MACH's benchmark claims are evaluated on held-out test splits and its component contributions are tested by ablation; the conflict-residual definition in Eq. (6) is a modeling choice, not a derivation that reduces the prediction to its inputs.

full rationale

MACH is trained with supervised cross-entropy, hierarchical alignment, prototype regularization, and supervised contrastive losses on train splits, and all headline accuracies are reported on held-out test sets with five-run averaging. The central SOTA claim therefore does not reduce to a fitted parameter or to a self-citation: no load-bearing result is imported from the authors' prior work, and no referenced theorem is used to forbid alternatives. The only definitional element is Eq. (6), where conflict c^(l) is defined as the pairwise absolute difference between the agreement-updated anchors u^(l)_s after hypergraph propagation. This makes the conflict pathway residual and complementary to the agreement pathway by construction, but that is an architectural modeling decision, not a circular derivation: the paper does not claim to derive conflict from independent evidence, nor does it fit a parameter to the target metric and then rename that fit as a prediction. Similarly, Table II's comparison may be confounded by the Qwen2.5-Omni-7B backbone (the baselines are not stated to use the same encoder), and the sentence claiming "best performance across all reported metrics" is inaccurate because HIER's MELD-DA macro F1 (54.80) exceeds MACH's (52.81); these are correctness and rigor concerns outside the circularity definition. No circular step is evidenced by an equation-to-equation reduction, so the appropriate finding is no significant circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 1 invented entities

MACH rests on the domain premise that agreement and conflict are reusable interaction patterns, on the modeling choice that conflict equals residual pairwise differences after agreement propagation, and on several unstated hyperparameters that are deferred to the supplementary. No physical entities are introduced; the latent agreement and conflict spaces are internal modeling constructs.

free parameters (3)

Prototype bank size K and top-r activated prototypes = not stated in main text
The number of prototypes per interaction unit and the number of activated prototypes per hyperedge control sparsity and capacity; values are deferred to the supplementary material.
Loss weights lambda_cls, lambda_1..lambda_4 and temperature tau = not stated in main text
These coefficients balance classification, hierarchical alignment, supervised contrastive, and prototype losses, and tau controls assignment sharpness; they are chosen by the authors and affect every reported number.
Conflict prototype count K_c and top-r_c activation = not stated in main text
The size and sparsity of the conflict prototype bank shape the conflict pathway, but no concrete value is given in the main artifact.

assumptions (5)

domain assumption Cross-modal agreement and conflict are reusable higher-order structures shared across semantically related utterances
Section I states this hypothesis directly, and the entire prototype-hypergraph design depends on agreement and conflict patterns being transferable across different surface content.
ad hoc to paper Conflict can be captured by pairwise absolute differences between updated agreement anchors
Eq. (6) defines conflict exclusively from L1 residual differences after agreement hypergraph propagation, so informative disagreement must be expressible as this residual magnitude.
domain assumption Frozen Qwen2.5-Omni-7B representations can be projected into a common interaction space without losing modality-specific semantics
Eq. (1) projects text, audio, and video embeddings into a shared d-dimensional space; if the encoder embeddings are not comparable, the whole anchor hierarchy is compromised.
standard math Standard normalized hypergraph convolution as defined by Feng et al. [17]
Eq. (5) uses the HGNN propagation rule from the cited reference, which is taken as a trusted background result.
domain assumption InfoNCE, supervised contrastive, and prototype regularization losses improve intent classification
Equations (9) to (11) assume that aligning anchor representations, compacting same-label conflict features, and diversifying prototypes transfer to better held-out intent prediction.

invented entities (1)

Agreement and conflict latent interaction spaces A and C
purpose: Serve as complementary internal representations that jointly characterize communicative intent instead of a single fused embedding
These are internal model constructs with no external falsifiable prediction; the paper validates them only through downstream accuracy and ablations, so they carry no independent evidence outside the model.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Modality Agreement- and Conflict-Aware Prototype Hypergraph Learning for Multimodal Intent Understanding." pith.science (2026). https://pith.science/paper/ZMC5RZPM

@misc{pith2026260804054,
  author       = {Pith},
  title        = {Pith review of: Modality Agreement- and Conflict-Aware Prototype Hypergraph Learning for Multimodal Intent Understanding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZMC5RZPM}},
  note         = {Machine review of arXiv:2608.04054}
}

read the original abstract

Multimodal intent recognition requires understanding not only what textual, acoustic, and visual signals share, but also how they disagree. Such disagreement is frequently class-informative; for example, lexical positivity accompanied by incongruent vocal or facial behavior may indicate sarcasm or taunting, yet most fusion methods either encourage modality alignment or treat inconsistency as uncertainty to be suppressed. We propose MACH (Modality Agreement- and Conflict-aware prototype Hypergraph), a hierarchical prototype-hypergraph framework that represents multimodal agreement and conflict as distinct, recurring relational structures. MACH progressively composes unimodal representations into bimodal and trimodal abstractions. At each applicable level, modality-composition anchors activate sparse agreement prototype hypergraphs that capture reusable consensus patterns, while a separate conflict pathway maps cross-modal discrepancies to dedicated conflict prototype hypergraphs. The two pathways are combined through a feature-wise, sample-adaptive arbitration mechanism, enabling the model to preserve informative disagreement while suppressing incidental modality noise. A progressive optimization strategy stabilizes the interdependent hierarchy before joint agreement-conflict learning. Experiments on benchmark datasets demonstrate the effectiveness of the proposed formulation, while component and robustness analyses validate the distinct roles of hierarchical composition, prototype-mediated semantic refinement, and agreement-conflict arbitration.

Figures

Figures reproduced from arXiv: 2608.04054 by the authors.

**Figure 1.** MACH full framework. mapping Φ : X → I, where I denotes the latent interaction space. We decompose Φ = (Φagr, Φcon), to obtain complementary agreement and conflict representations, A = Φagr(X), C = Φcon(X), where A captures cross-modal semantic agreement and C captures informative semantic conflict. Rather than absorbing disagreement into feature fusion or treating it as modality unreliability, we model agreement… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

24 extracted references · 11 canonical work pages

[1]

A survey on multi-modal intent recognition: Recent advances and new frontiers,

Z. Zhu, F. Zhang, Y . Zhang, J. Sun, Z. Huang, Q. Long, B. Xing, and X. Wu, “A survey on multi-modal intent recognition: Recent advances and new frontiers,”Findings of the Association for Computational Linguistics: EMNLP 2025, pp. 15223–15236, 2025

2025
[2]

Deep learning approaches for multimodal intent recognition: A survey,

J. Zhao, Y . Wen, Q. Li, M. Hu, Y . Zhou, J. Xue, J. Wu, Y . Gao, Z. Wen, J. Tao,et al., “Deep learning approaches for multimodal intent recognition: A survey,”arXiv preprint arXiv:2507.22934, 2025

arXiv 2025
[3]

Multimodal transformer for unaligned multimodal language sequences,

Y .-H. H. Tsai, S. Bai, P. P. Liang, J. Z. Kolter, L.-P. Morency, and R. Salakhutdinov, “Multimodal transformer for unaligned multimodal language sequences,” inProceedings of the 57th Annual Meeting of the Association for Computational Linguistics(A. Korhonen, D. Traum, and L. Màrquez, eds.), (Florence, Italy), pp. 6558–6569, Association for Computational...

2019
[4]

Integrating multimodal information in large pretrained transformers,

W. Rahman, M. K. Hasan, S. Lee, A. B. Zadeh, C. Mao, L.-P. Morency, and E. Hoque, “Integrating multimodal information in large pretrained transformers,” inProceedings of the 58th annual meeting of the association for computational linguistics, pp. 2359–2369, 2020

work page 2020
[5]

Adaptive multimodal fusion: dynamic attention allocation for intent recognition,

B. Hu, K. Zhang, Y . Zhang, and Y . Ye, “Adaptive multimodal fusion: dynamic attention allocation for intent recognition,” inProceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty- Seventh Conference on Innovative Applications of Artificial Intelligence and Fifteenth Symposium on Educational Advances in Artificial Intelli- g...

2025
[6]

Multimodal intent recognition based on text-guided cross-modal attention,

Z. Li, J. Peng, X. Lin, and Z. Cai, “Multimodal intent recognition based on text-guided cross-modal attention,”Appl. Intell., vol. 55, no. 7, p. 690, 2025. 8

2025
[7]

Graph mixture of experts with differential cross-attention alignment for multimodal intent recognition,

S. Sun, W. An, Q. Liu, F. Nan, J. Nie, Z. Zeng, X.-S. Hua, Y . Wu, and F. Tian, “Graph mixture of experts with differential cross-attention alignment for multimodal intent recognition,”Knowledge-Based Sys- tems, vol. 349, p. 116392, 2026

work page 2026
[8]

Mitigating Multi- modal Inconsistency via Cognitive Dual-Pathway Reasoning for Intent Recognition,

Y . Wang, P. Wang, Y . Chi, Z. Gou, and K. Gao, “Mitigating Multi- modal Inconsistency via Cognitive Dual-Pathway Reasoning for Intent Recognition,” inProceedings of the 2026 International Conference on Multimedia Retrieval, pp. 797–806, 2026

2026

Show all 24 references

[9]

Who should I trust? explicit confidence- focused multimodal intent recognition,

Y . Liu, Q. Yang, and L. Lu, “Who should I trust? explicit confidence- focused multimodal intent recognition,” inProceedings of the For- tieth AAAI Conference on Artificial Intelligence and Thirty-Eighth Conference on Innovative Applications of Artificial Intelligence and Sixt...

2026
[10]

Evolutionary Multimodal Reasoning via Hierarchical Semantic Representation for Intent Recognition,

Q. Zhou, H. Xu, Y . Gu, Y . Wang, S. Li, and H. Zhang, “Evolutionary Multimodal Reasoning via Hierarchical Semantic Representation for Intent Recognition,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14979–14989, 2026

2026
[11]

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

D. Hazarika, R. Zimmermann, and S. Poria, “Misa: Modality-invariant and -specific representations for multimodal sentiment analysis,” inPro- ceedings of the 28th ACM International Conference on Multimedia, MM ’20, (New York, NY , USA), p. 1122–1131, Association for Computing M...

2020
[12]

Token-level contrastive learning with modality-aware prompting for multimodal intent recognition,

Q. Zhou, H. Xu, H. Li, H. Zhang, X. Zhang, Y . Wang, and K. Gao, “Token-level contrastive learning with modality-aware prompting for multimodal intent recognition,” inProceedings of the AAAI conference on artificial intelligence, vol. 38, pp. 17114–17122, 2024

2024
[13]

Sdif-da: A shallow- to-deep interaction framework with data augmentation for multi-modal intent detection,

S. Huang, L. Qin, B. Wang, G. Tu, and R. Xu, “Sdif-da: A shallow- to-deep interaction framework with data augmentation for multi-modal intent detection,” inICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 10206– 10210, IEEE, 2024

2024
[14]

InMu-Net: Advancing Multi-modal Intent Detection via In- formation Bottleneck and Multi-sensory Processing,

Z. Zhu, X. Cheng, Z. Chen, Y . Chen, Y . Zhang, X. Wu, Y . Zheng, and B. Xing, “InMu-Net: Advancing Multi-modal Intent Detection via In- formation Bottleneck and Multi-sensory Processing,” inProceedings of the 32nd ACM International Conference on Multimedia, MM ’24, (New York,...

2024
[15]

Prototype-based intent perception,

B. Wang, K. Yang, Y . Zhao, T. Long, and X. Li, “Prototype-based intent perception,”IEEE Transactions on Multimedia, vol. 25, pp. 8308–8319, 2023

2023
[16]

Mvcl-daf++: En- hancing multimodal intent recognition via prototype-aware contrastive alignment and coarse-to-fine dynamic attention fusion,

H. Huang, Y . Han, L. Zhang, B. Li, and Y . He, “Mvcl-daf++: En- hancing multimodal intent recognition via prototype-aware contrastive alignment and coarse-to-fine dynamic attention fusion,”arXiv preprint arXiv:2509.17446, 2025

2025 arXiv
[17]

Hypergraph neural networks,

Y . Feng, H. You, Z. Zhang, R. Ji, and Y . Gao, “Hypergraph neural networks,” inProceedings of the Thirty-Third AAAI Conference on Ar- tificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational A...

2019
[18]

Hybrid-scale heterogeneous graph fusion for multimodal intent recognition,

R. Liu, Y . Ma, and H. Li, “Hybrid-scale heterogeneous graph fusion for multimodal intent recognition,”IEEE Transactions on Affective Computing, 2026

2026
[19]

Hypermodal: Dynamic hypergraph contrastive learning for multi-modal representation,

L. Al Kulaib, “Hypermodal: Dynamic hypergraph contrastive learning for multi-modal representation,” in2025 IEEE International Conference on Data Mining (ICDM), pp. 1023–1029, IEEE, 2025

2025
[20]

Hypergcl: multi-modal graph contrastive learning via learnable hypergraph views,

K. M. Saifuddin, S. Ji, and E. Akbas, “Hypergcl: multi-modal graph contrastive learning via learnable hypergraph views,” in2025 Interna- tional Joint Conference on Neural Networks (IJCNN), pp. 1–8, IEEE, 2025

2025
[21]

Supervised contrastive learn- ing,

P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y . Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, “Supervised contrastive learn- ing,” inProceedings of the 34th International Conference on Neural Information Processing Systems, NIPS ’20, (Red Hook, NY , USA), Curran Associ...

2020
[22]

MIntRec: A new dataset for multimodal intent recognition,

H. Zhang, H. Xu, X. Wang, Q. Zhou, S. Zhao, and J. Teng, “MIntRec: A new dataset for multimodal intent recognition,” inProceedings of the 30th ACM international conference on multimedia, pp. 1688–1697, 2022

2022
[23]

MIntRec2.0: A large-scale benchmark dataset for multimodal intent recognition and out-of-scope detection in conversations,

H. Zhang, X. Wang, H. Xu, Q. Zhou, K. Gao, J. Su, W. Li, Y . Chen, et al., “MIntRec2.0: A large-scale benchmark dataset for multimodal intent recognition and out-of-scope detection in conversations,” inThe Twelfth International Conference on Learning Representations, 2024

2024
[24]

MELD: A multimodal multi-party dataset for emotion recognition in conversations,

S. Poria, D. Hazarika, N. Majumder, G. Naik, E. Cambria, and R. Mihal- cea, “MELD: A multimodal multi-party dataset for emotion recognition in conversations,” inProceedings of the 57th Annual Meeting of the Association for Computational Linguistics(A. Korhonen, D. Traum, and L...

2019

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.

[1] [1]

A survey on multi-modal intent recognition: Recent advances and new frontiers,

Z. Zhu, F. Zhang, Y . Zhang, J. Sun, Z. Huang, Q. Long, B. Xing, and X. Wu, “A survey on multi-modal intent recognition: Recent advances and new frontiers,”Findings of the Association for Computational Linguistics: EMNLP 2025, pp. 15223–15236, 2025

2025

[2] [2]

Deep learning approaches for multimodal intent recognition: A survey,

J. Zhao, Y . Wen, Q. Li, M. Hu, Y . Zhou, J. Xue, J. Wu, Y . Gao, Z. Wen, J. Tao,et al., “Deep learning approaches for multimodal intent recognition: A survey,”arXiv preprint arXiv:2507.22934, 2025

arXiv 2025

[3] [3]

Multimodal transformer for unaligned multimodal language sequences,

Y .-H. H. Tsai, S. Bai, P. P. Liang, J. Z. Kolter, L.-P. Morency, and R. Salakhutdinov, “Multimodal transformer for unaligned multimodal language sequences,” inProceedings of the 57th Annual Meeting of the Association for Computational Linguistics(A. Korhonen, D. Traum, and L. Màrquez, eds.), (Florence, Italy), pp. 6558–6569, Association for Computational...

2019

[4] [4]

Integrating multimodal information in large pretrained transformers,

W. Rahman, M. K. Hasan, S. Lee, A. B. Zadeh, C. Mao, L.-P. Morency, and E. Hoque, “Integrating multimodal information in large pretrained transformers,” inProceedings of the 58th annual meeting of the association for computational linguistics, pp. 2359–2369, 2020

work page 2020

[5] [5]

Adaptive multimodal fusion: dynamic attention allocation for intent recognition,

B. Hu, K. Zhang, Y . Zhang, and Y . Ye, “Adaptive multimodal fusion: dynamic attention allocation for intent recognition,” inProceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty- Seventh Conference on Innovative Applications of Artificial Intelligence and Fifteenth Symposium on Educational Advances in Artificial Intelli- g...

2025

[6] [6]

Multimodal intent recognition based on text-guided cross-modal attention,

Z. Li, J. Peng, X. Lin, and Z. Cai, “Multimodal intent recognition based on text-guided cross-modal attention,”Appl. Intell., vol. 55, no. 7, p. 690, 2025. 8

2025

[7] [7]

Graph mixture of experts with differential cross-attention alignment for multimodal intent recognition,

S. Sun, W. An, Q. Liu, F. Nan, J. Nie, Z. Zeng, X.-S. Hua, Y . Wu, and F. Tian, “Graph mixture of experts with differential cross-attention alignment for multimodal intent recognition,”Knowledge-Based Sys- tems, vol. 349, p. 116392, 2026

work page 2026

[8] [8]

Mitigating Multi- modal Inconsistency via Cognitive Dual-Pathway Reasoning for Intent Recognition,

Y . Wang, P. Wang, Y . Chi, Z. Gou, and K. Gao, “Mitigating Multi- modal Inconsistency via Cognitive Dual-Pathway Reasoning for Intent Recognition,” inProceedings of the 2026 International Conference on Multimedia Retrieval, pp. 797–806, 2026

2026