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Reliability-aware Cross-sample Enhancement for Robust Multimodal Sentiment Analysis

Menghua Jiang, Haokai Gao, Xiangui Kang, Haifeng Hu, Sijie Mai

arXiv:2609.30470v1cs.LG

TL;DR

Real-world multimodal sentiment analysis faces incomplete, noisy, or uncertain inputs, while existing approaches often address missingness and noise separately. RCE combines uncertainty-aware compression, cross-sample enhancement, and reliability-aware fusion; experiments report consistent gains across diverse evaluation settings.

  • Problem

    Real-world MSA inputs can be incomplete, noisy, or uncertain, while existing methods often address missing modalities and noise separately.

    Source: §1 · pp. 1–2

  • Method

    RCE models modality uncertainty for adaptive compression, retrieves reliable cross-sample cues to enhance representations, and uses reliability-aware multilevel fusion.

    Source: §1 · p. 2, §5 · p. 10

  • Results

    RCE consistently outperforms existing methods across four benchmark datasets and three evaluation settings; on UR-FUNNY and MUStARD, it exceeds the best baselines by over 3.2% and 1.5% in accuracy, respectively.

    Source: §1 · p. 2, §4.2 · p. 8

  • Takeaways & Limitations

    The authors conclude that RCE provides robust multimodal representations across full, noisy, and missing-modality settings.

    Source: §5 · p. 10

    RCE adds hyperparameters and parameters, and dataset-specific tuning may still be needed despite general stability within moderate parameter ranges.

    Source: §5 · p. 10

Abstract

from arXiv · show

Multimodal Sentiment Analysis (MSA) aims to infer human emotions from multiple modalities such as text, audio, and vision. In practice, inputs are often corrupted by noise and missing modalities, which degrades performance. Existing methods typically address these challenges in isolation, limiting their effectiveness in realistic settings. To address this limitation, we propose a Reliability-aware Cross-sample Enhancement (RCE) framework. Specifically, RCE first introduces an adaptive variational information bottleneck to model modality-wise uncertainty and perform quality-aware information compression, thereby suppressing redundant noise in unreliable modalities. Furthermore, we design a reliability-aware cross-sample enhancement strategy that retrieves high-confidence, semantically consistent neighbors from a large candidate pool to enrich and calibrate current representations, effectively alleviating information deficiency caused by missing modalities. Building upon this, RCE integrates cross-modal interactions with a multilevel reliability-aware fusion mechanism to adaptively aggregate information across modalities and enhancement stages, leading to more robust multimodal representations. Extensive experiments demonstrate that RCE consistently outperforms state-of-the-art methods across full, noisy, and missing-modality settings.

1 Introduction

Real-world MSA must contend with missing, noisy, and uncertain modalities, while prior work often treats missingness and noise separately. RCE combines uncertainty-aware compression, cross-sample enhancement, and reliability-aware fusion for robust sentiment analysis across diverse conditions.

1 Introduction

  • Existing methods often assume ideal full-modality access or address missingness and noise as separate problems, using alignment, information bottlenecks, reconstruction, or cross-sample augmentation.Examples include MODS and DecAlign for full modalities, MIB and OMIB for noise, and CyIN and HME for missing modalities.Source: §1 · pp. 1–2
  • Real-world missingness and noise can be unknown and dynamic, while robustness to incomplete inputs may come at a cost to full-modality performance.The introduction also notes that many denoising strategies do not explicitly model modality reliability despite noise introducing aleatoric uncertainty.Source: §1 · p. 2
  • RCE combines modality-wise uncertainty modeling, quality-aware compression, reliable cross-sample enhancement, and multilevel reliability-aware fusion to handle noise and missing modalities in one framework.Its components include adaptive variational information bottleneck, retrieval of semantically consistent, high-confidence neighbors, and hyper-modality generation.Source: §1 · p. 2
  • RCE is designed for full-modality, noisy, and missing-modality conditions without requiring each condition to be handled separately.The introduction reports consistent performance across these diverse scenarios.Source: §1 · p. 2
  • Experiments on four benchmark datasets and three evaluation settings report that RCE consistently outperforms existing methods with stronger robustness.The settings cover full-modality, noisy, and missing-modality conditions.Source: §1 · p. 2

2 Related Works

Prior MSA work commonly assumes complete modalities, while missing-modality methods use reconstruction, distillation, or cross-sample information. RCE addresses limitations of existing cross-sample approaches by retrieving high-confidence, semantically consistent neighbors from a larger candidate pool.

2.1 Multimodal Sentiment Analysis

  • Most MSA methods assume all modalities are available at inference and focus on fusion; examples include cross-modal alignment, dynamic primary-modality selection, and personality-aware embeddings.The cited approaches include DecAlign, MODS, and PSA-MF.Source: §2.1 · p. 2

2.2 Multimodal Sentiment Analysis with Missing Modalities

  • Missing-modality methods reconstruct unobserved inputs, distill from fully observed teachers, or augment representations with cross-sample information.Examples include IMDer and CyIN for reconstruction, HRLF for teacher–student distillation, and CorrKD and HME for cross-sample approaches.Source: §2.2 · p. 3
  • Many missing-modality approaches assume noise-free data, and mini-batch retrieval can limit neighborhood stability or introduce negative enhancement from opposite-polarity samples.RCE instead retrieves semantically consistent, high-confidence neighbors from a large-scale candidate pool.Source: §2.2 · p. 3

3 Methodology

RCE processes multimodal inputs through reliability-aware compression and enhancement, then generates hyper-modality representations and fuses information across modalities and stages. Its method formulates sentiment prediction from text, audio, and visual features, and specifies separate mechanisms for preprocessing, uncertainty-aware compression, neighbor enhancement, and multilevel fusion.

3.1 Task Formulation

  • MSA predicts a speaker’s emotional state from text, acoustic, and visual video-segment features, with either continuous sentiment scores or discrete emotion categories as outputs.The model learns a prediction function over the multimodal input sequences.Source: §3.1 · pp. 3–4

3.2 Pre-Processing

  • Each modality’s feature sequence is projected into a shared embedding dimension, encoded by a Transformer with positional information, and globally max-pooled into an initial representation.The projection uses modality-specific one-dimensional convolutions.Source: §3.2 · p. 4

3.3 Adaptive Variational Information Bottleneck

  • AVIB maps modality features to vMF posteriors, using concentration as a reliability proxy to adapt compression and encourage noisy modalities toward the uniform prior.Auxiliary modality-specific predictions are jointly optimized with KL regularization; concentrated posteriors incur higher information cost and are retained when discriminative.Source: §3.3 · pp. 4–5

3.4 Reliability-aware Modality Enhancement

  • RME retrieves top-k neighbors from a modality-specific memory bank using stable directional similarity, filtering weak matches and weighting neighbors by label consistency and confidence.The bank is used when populated and otherwise retrieval falls back to the current mini-batch; RME is training-only and supports incomplete data without explicit modality reconstruction.Source: §3.4 · pp. 5–6

3.5 Hyper-Modality Generation and Multilevel Fusion

  • A Perceiver-style latent Transformer and cross-attention construct modality-level hyper-representations that combine other modalities’ complementary information with shared context.A separate shared latent Transformer takes the original unimodal representations as input to model shared multimodal context.Source: §3.5 · p. 6
  • Reliability-aware multilevel fusion combines each original modality representation with its hyper-modality representation, then weights modalities and models cross-level interactions before prediction.The vMF concentration modulates intra-modality fusion weights; four tokens—three intra-modality representations and one global representation—are processed by a Transformer encoder.Source: §3.5 · p. 7

4 Experiments

RCE is evaluated across multimodal sentiment, humor, and sarcasm tasks, including full- and missing-modality settings. Results favor RCE across benchmarks, while ablations and further analyses examine component contributions and enhancement behavior.

4.1 Experimental Setup

  • Experiments cover MSA under full, noisy, and missing-modality settings, plus humor and sarcasm detection; datasets are CMU-MOSI, CMU-MOSEI, UR-FUNNY, and MUStARD.Noisy and missing modalities are each evaluated under two protocols; the comparison includes recent full-modality, information-bottleneck, and uncertainty-aware fusion methods.Source: §4.1 · p. 7

4.2 Quantitative Results

  • RCE achieves the best performance on nearly all full-modality metrics, significantly outperforming state-of-the-art methods on Acc7 and MAE.The comparison covers CMU-MOSI and CMU-MOSEI.Source: §4.2 · p. 8, Table 1 · p. 8
  • Under random missing rates, RCE consistently outperforms HME and CyIN across most rates, without modality-specific tuning.Figure 2 presents the CMU-MOSEI comparison under the random missing protocol.Source: §4.2 · p. 8, Figure 2 · p. 8
  • RCE exceeds the best-performing baselines in accuracy by over 3.2% on UR-FUNNY and 1.5% on MUStARD.These results extend evaluation to multimodal humor and sarcasm detection.Source: §4.2 · p. 8, Figure 3 · p. 9

4.3 Ablation Study

  • Removing most components degrades performance; hyper-modality generation, the memory bank, and AVIB are the most influential components.On CMU-MOSI, removing HMG lowers Acc7 from 50.22 to 46.72 and raises MAE from 0.589 to 0.637; removing RF slightly improves Acc7 but worsens MAE.Source: §4.3 · pp. 8–9, Table 2 · p. 9

4.4 Further Analysis

  • Further analyses also include parameter sensitivity, testing stability, model complexity, and theoretical complexity.These analyses are reported in the appendix.Source: §4.4 · p. 9
  • RCE’s hyper-modality representations form distinct t-SNE clusters, while negative enhancement is reduced versus HME across modalities.On CMU-MOSI, negative enhancement rates fall from around 46% to below 7%; the reported analyses also note smaller improvement for audio on CMU-MOSEI.Source: §4.4 · p. 9, Table 3 · p. 9

5 Conclusion

RCE unifies reliability estimation, cross-sample enhancement, and multilevel fusion for robust sentiment analysis across full, noisy, and missing-modality settings. The conclusion also notes implementation complexity and describes the limited role of LLMs.

5 Conclusion

  • RCE estimates modality reliability, retrieves reliable cross-sample cues, and applies reliability-aware multilevel fusion under full, noisy, and missing-modality settings.The framework is designed to handle these conditions without separate treatment.Source: §5 · p. 10
  • RCE adds hyperparameters and parameters; despite stability within moderate ranges, dataset-specific tuning may still be needed.Future work will explore adaptive and lightweight enhancement strategies.Source: §5 · p. 10

The Use of Large Language Models (LLMs)

  • LLMs were used solely for language polishing; the authors retain responsibility for scientific content and its analysis and interpretation.The authors report that all LLM-assisted content was reviewed and verified.Source: The Use of Large Language Models (LLMs) · p. 16

A Notations

  • Table 4 provides definitions for the paper’s commonly used notation, including reliability weights, fusion weights, and joint representations.The listed notation includes γ_i, η_i, H, U_i, and r_i.Source: A Notations · p. 16, Table 4 · p. 16

B.1 Multimodal Sentiment Analysis with Noisy Modalities

Prior noise-robust MSA work includes augmentation, filtering, and representation regularization, but these approaches have limitations in generalization or reliability modeling. RCE uses uncertainty-aware compression to adapt to modality quality.

B.1 Multimodal Sentiment Analysis with Noisy Modalities

  • Noise-augmentation methods simulate perturbations and develop defenses, but dedicated defenses for different noise types can be complex and may generalize poorly.Examples include Robust-MSA [35] and MSA-Robustness [11].Source: §B.1 · p. 17
  • Filtering methods use dynamic gating [54] or unimodal losses, but may lack explicit reliability modeling or depend on difficult-to-define absolute labels.The passage also notes that constructing such labels may introduce training noise.Source: §B.1 · p. 17
  • Unlike bottleneck methods that typically compress modalities equally, RCE’s vMF-space AVIB adapts compression to modality reliability, preserving informative signals and suppressing noise.Prior approaches include MIB [33], ITHP [51], OMIB [49], and KAN-MCP.Source: §B.1 · p. 17

C Methodological Details

The vMF representation models sample-level modality uncertainty, with concentration serving as a reliability proxy. Reservoir sampling maintains a fixed-size, order-agnostic historical sample pool for neighbor retrieval.

C.1 von Mises–Fisher Distribution

  • Each modality’s feature representation is mapped to a vMF distribution, whose concentration indicates sample-level certainty and serves as a modality reliability proxy.This supports adaptive, reliability-aware representation learning.Source: §C.1 · pp. 17–18

C.2 Reservoir Sampling Strategy

  • Reservoir sampling retains a uniformly sampled subset of historical samples under a fixed capacity, regardless of their arrival order once the bank is full.After observing t samples, each is retained with probability N_b/t when t exceeds capacity.Source: §C.2 · p. 19
  • The method maintains separate modality-specific banks, rebuilding them each epoch for retrieval; before a bank fills, candidates come from the current batch.Once populated, the bank supplies candidates for RME neighbor retrieval.Source: §C.2 · p. 19

D.1 Datasets Information

The appendix documents datasets, baselines, feature extraction, implementation, and evaluation metrics, and identifies a fixed-missing-protocol comparison table. The benchmarks cover sentiment, humor, and sarcasm tasks.

D.1 Datasets Information

  • The four benchmarks cover sentiment (CMU-MOSI and CMU-MOSEI), humor (UR-FUNNY), and sarcasm (MUStARD).CMU-MOSI and CMU-MOSEI use sentiment scores; UR-FUNNY and MUStARD use humor and sarcasm labels, respectively.Source: §D.1 · pp. 19–20

D.2 Baselines

  • The baselines include uncertainty-aware fusion and missing-modality methods, alongside information-bottleneck and multimodal interaction approaches.Examples described include PML, HME, CyIN [27], MIB [33], QMF [65], and MIL.Source: §D.2 · pp. 20–21

D.3 Feature Extraction

  • Text features use pretrained encoders, while acoustic and visual sequences use COVAREP and Facet or OpenFace features.Text encoders differ by dataset; Figure 2 comparisons with CyIN use BERT for RCE and HME.Source: §D.3 · pp. 21–22

D.4 Implementation Details

  • Experiments use AdamW with linear warm-up, and dataset-specific settings vary feature dimensions, memory-bank capacities, dropout, and AVIB coefficients.Configuration selection uses thirty random validation trials and lowest validation MAE.Source: §D.4 · p. 22
  • Several baselines are re-implemented with RCE’s feature extraction strategy and thirty validation search trials; other results are taken from prior papers.The re-implementations use official codebases and search ranges reported in the original papers.Source: §D.4 · pp. 22–23

D.5 Evaluation Metrics

  • MSA evaluation reports sentiment regression and classification metrics, while humor and sarcasm evaluation reports binary accuracy.The listed MSA measures include Acc7, Acc2, weighted F1, MAE, and Pearson correlation.Source: §D.5 · pp. 23–24

E More Experimental Results

  • Table 6 presents a performance comparison of RCE and baseline methods under the fixed missing protocol.The supplied caption identifies the comparison scope but does not provide cell values.Source: Table 6 · p. 24

E.1 Results under Fixed Missing Protocols

Under fixed missing-modality protocols, RCE achieves the best performance in 24 of 30 metric comparisons. It performs strongly across text-only and multimodal availability patterns.

E.1 Results under Fixed Missing Protocols

  • With only text available, RCE leads on four of five metrics on CMU-MOSI and three of five on CMU-MOSEI.The reported results indicate reliable predictions under highly sparse modality conditions.Source: §E.1 · pp. 24–25
  • With text and audio, RCE beats all baselines on every metric across both datasets; with text and vision, it leads all five CMU-MOSEI metrics and is competitive on CMU-MOSI.For CMU-MOSI with text and vision, it obtains the lowest MAE and highest correlation, while Acc2 and F1 remain close to the best baseline.Source: §E.1 · p. 25
  • RCE achieves the best performance in 24 of 30 metric comparisons under the fixed missing protocol.The comparisons span different deterministic modality-availability patterns.Source: §E.1 · p. 25

E.2 Results under Noisy Modalities

RCE performs strongly under Gaussian and Salt-pepper corruption, with particularly strong results at severe noise levels. Its performance is less consistent under medium Salt-pepper noise on CMU-MOSEI.

E.2 Results under Noisy Modalities

  • At Gaussian noise intensity 1.0, RCE leads most metrics, including Acc7, Acc2, F1, and MAE on CMU-MOSI and Acc7, MAE, and Corr on CMU-MOSEI.Its Corr on CMU-MOSI and Acc2 and F1 on CMU-MOSEI remain close to the best results.Source: Table 7 · p. 25, §E.2 · p. 26
  • At Gaussian noise intensity 5.0, RCE leads Acc2 and F1 on CMU-MOSI and Acc2, F1, and MAE on CMU-MOSEI.HME has better Acc7, MAE, and Corr on CMU-MOSI at this intensity.Source: Table 7 · p. 25, §E.2 · p. 26
  • At Gaussian noise intensity 10.0, RCE leads all five CMU-MOSI metrics and leads Acc7, Acc2, and MAE on CMU-MOSEI.It remains close to the best Corr on CMU-MOSEI.Source: Table 7 · p. 25, §E.2 · p. 27
  • At Salt-pepper intensity 1.0, RCE leads Acc7, Acc2, F1, and MAE on CMU-MOSI and Acc7, Acc2, F1, and Corr on CMU-MOSEI.Salt-pepper noise replaces selected valid acoustic and visual feature elements with sample-level minimum or maximum values; acoustic and visual noise is applied at test time.Source: §E.2 · pp. 25–27, Table 8 · p. 26
  • At Salt-pepper intensity 10.0, RCE leads Acc7, Acc2, F1, and MAE on CMU-MOSI and Acc7, Acc2, MAE, and Corr on CMU-MOSEI.The authors describe its performance as generally resilient to severe outlier-style corruption.Source: Table 8 · p. 26, §E.2 · p. 27
  • At Salt-pepper intensity 5.0, RCE leads Acc2, F1, MAE, and Corr on CMU-MOSI, but KAN-MCP and HME lead on several CMU-MOSEI metrics.RCE remains close to the best Acc7 on CMU-MOSI.Source: Table 8 · p. 26, §E.2 · p. 27

E.3 Detailed Ablation Study

The ablations indicate that RCE's components contribute in different ways, with the complete model generally yielding the most balanced results across datasets and metrics.

E.3 Detailed Ablation Study

  • The complete RCE achieves the best or competitive results on most metrics, including the best Acc2, F1, MAE, and Corr on CMU-MOSI.Removing ES drops Acc7 from 50.22 to 45.69 and raises MAE from 0.589 to 0.654; removing HMG also lowers performance, especially on Acc7 and MAE.Source: Table 9 · p. 27, §E.3 · p. 28
  • Removing AVIB weakens all CMU-MOSI metrics, while disabling the memory bank lowers Acc7, MAE, and Corr relative to RCE.The authors associate AVIB with reducing noisy and redundant information, and report more stable neighborhood information from historical reliable samples than mini-batch-only retrieval.Source: §E.3 · pp. 27–28
  • On CMU-MOSEI, some ablations improve individual metrics, but RCE has the best Corr and nearly the best MAE; removing RF improves Acc2 and F1 but worsens MAE and Corr.The authors interpret this as reliability-aware fusion being more helpful for fine-grained sentiment intensity estimation.Source: §E.3 · pp. 27–28

E.4 Negative Enhancement Analysis

RCE reports fewer negative enhancement cases than HME on both datasets, with a particularly large reduction for text on CMU-MOSEI.

E.4 Negative Enhancement Analysis

  • On CMU-MOSI, RCE's negative enhancement rates are 2.60% for text, 5.90% for audio, and 6.48% for vision, compared with nearly 46% for HME across modalities.The analysis counts a case when the weighted average label of enhancement sources has opposite sentiment polarity to the current sample.Source: §E.4 · pp. 28–29, Table 10 · p. 28
  • On CMU-MOSEI, RCE reduces text negative enhancement from 38.40% to 10.57% relative to HME, while the audio reduction is smaller.The authors suggest ambiguous and diverse acoustic cues may leave some sentiment-conflicting neighbors with non-negligible weights.Source: Table 10 · p. 28, §E.4 · p. 29

E.5 Parameter Sensitivity Analysis

RCE remains stable across moderate hyperparameter ranges and random initializations, while its added computational cost is reported as acceptable and tractable.

E.5 Parameter Sensitivity Analysis

  • β = 1e-3 is the default, while λ preferences vary by dataset and metric; moderate settings balance bottleneck regularization and prediction loss.On CMU-MOSEI, β = 1e-3 achieves strong Acc7 and lowest MAE; λ performs best around 0.4 for Acc7 and 1.0 for MAE.Source: §E.5 · pp. 29–30
  • Larger memory banks generally improve performance, but further capacity increases do not always help; Nb = 512 gives best Acc7 on both datasets.Lowest MAE occurs at Nb = 1024 on CMU-MOSI and at Nb = 1024 or 2048 on CMU-MOSEI.Source: §E.5 · p. 30
  • On CMU-MOSI, α around 0.4 yields the best Acc7 and MAE, while excessive label penalties reduce performance; CMU-MOSEI is relatively stable, favoring α = 0.1.A mild label penalty may suffice on CMU-MOSEI because it has more samples and more diverse sentiment expressions.Source: §E.5 · p. 30
  • Moderate neighbor selection works best: ρ around 0.5 gives the best Acc7 on CMU-MOSI, while ρ = 0.4 gives the best overall performance on CMU-MOSEI.Too few neighbors may provide insufficient complementary information, while too many may introduce less relevant or sentiment-conflicting samples.Source: §E.5 · p. 30
  • Across both datasets, RCE remains stable within moderate hyperparameter ranges despite slightly different preferred values.The authors conclude the reliability-aware enhancement and multilevel fusion mechanisms are not overly sensitive to hyperparameter choices.Source: §E.5 · pp. 30–31

E.6 Testing Stability Analysis

  • Five-seed testing under complete modalities shows narrow 95% confidence intervals on CMU-MOSI and CMU-MOSEI, indicating small variation across random initializations.The intervals summarize five runs for each dataset.Source: §E.6 · p. 31, Table 11 · p. 31

E.7 Model Complexity Analysis

  • RCE uses 12.660G FLOPs and 9.49GB memory; despite the largest parameter count, its 8.54s-per-epoch training time is below HME and KAN-MCP.The authors characterize its added cost as moderate and not prohibitive.Source: §E.7 · p. 31, Table 12 · p. 31

E.8 Theoretical Complexity Analysis

  • Additional complexity is dominated by memory-bank retrieval at O(MBN_bd); with M and N_b fixed in practice, RCE remains computationally tractable.Theoretical analysis assigns O(MBd^2) complexity to AVIB and the lightweight hyper-modality generation and multilevel fusion modules.Source: §E.8 · pp. 31–32
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