# Towards Robust Multimodal Sentiment Analysis with Incomplete Data

arXiv: https://arxiv.org/abs/2409.20012 · HTML: https://mlsanity.com/paper/2409.20012/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2005.03545/) (2020)
- [Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2102.04830/) (2021)
- [Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2109.00412/) (2021)
- [M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cues](https://mlsanity.com/paper/1911.05659/) (2019)
- [Semi-supervised Multi-modal Emotion Recognition with Cross-Modal Distribution Matching](https://mlsanity.com/paper/2009.02598/) (2020)

## Built on by (most cited first, 27 of 27)

- [Retrieval-Augmented Perception: High-Resolution Image Perception Meets Visual RAG](https://mlsanity.com/paper/2503.01222/) (2025)
- [Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis](https://mlsanity.com/paper/2602.16144/) (2026)
- [Multimodal Transformers are Hierarchical Modal-wise Heterogeneous Graphs](https://mlsanity.com/paper/2505.01068/) (2025)
- [Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts](https://mlsanity.com/paper/2506.10452/) (2025)
- [CyIN: Cyclic Informative Latent Space for Bridging Complete and Incomplete Multimodal Learning](https://mlsanity.com/paper/2602.04920/) (2026)
- [Cross-modal Prompting for Balanced Incomplete Multi-modal Emotion Recognition](https://mlsanity.com/paper/2512.11239/) (2025)
- [Towards Robust and Realible Multimodal Misinformation Recognition with Incomplete Modality](https://mlsanity.com/paper/2510.05839/) (2025)
- [DCER: Dual-Stage Compression and Energy-Based Reconstruction](https://mlsanity.com/paper/2602.04904/) (2026)
- [Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection](https://mlsanity.com/paper/2602.16161/) (2026)
- [ModalImmune: Immunity Driven Unlearning via Self Destructive Training](https://mlsanity.com/paper/2602.16197/) (2026)
- [Progressive Representation Learning for Multimodal Sentiment Analysis with Incomplete Modalities](https://mlsanity.com/paper/2603.09111/) (2026)
- [Enhance-then-Balance Modality Collaboration for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2604.12518/) (2026)
- [Controlling Decision Drift in Multimodal Sentiment Analysis with Missing Modalities](https://mlsanity.com/paper/2605.16889/) (2026)
- [Modality Disentangled Learning for Incomplete Multimodal Emotion Recognition: A Primitive Memory Distillation Perspective](https://mlsanity.com/paper/2608.30563/) (2026)
- [Buffer replay enhances the robustness of multimodal learning under missing-modality](https://mlsanity.com/paper/2511.23070/) (2025)
- [QA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of Experts for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2604.05704/) (2026)
- [General Incomplete Multimodal Learning via Dynamic Quality Perception](https://mlsanity.com/paper/2607.06943/) (2026)
- [Should Missing Modalities Always Be Necessary to Repair for Multi-modal Sentiment Analysis?](https://mlsanity.com/paper/2607.17262/) (2026)
- [Rethinking Modality Reliability in Multimodal Sentiment Analysis with Incomplete Observations](https://mlsanity.com/paper/2608.03611/) (2026)
- [C2MOE: Consistency and Complementarity-guided Mixture of Experts for Incomplete Multimodal Emotion Learning](https://mlsanity.com/paper/2608.04013/) (2026)
- [MIDAS: Mutual Information Disentanglement with Uncertainty-Aware Fusion for Incomplete Multimodal Sentiment Analysis](https://mlsanity.com/paper/2608.09986/) (2026)
- [Robust Incomplete Multimodal Sentiment Analysis via Iterative Proxy Correction](https://mlsanity.com/paper/2608.19971/) (2026)
- [Contrastive Mixed Prompt Learning for Incomplete Multimodal Sentiment Analysis with Unseen Modality Combination](https://mlsanity.com/paper/2608.20019/) (2026)
- [Robust Multimodal Sentiment Analysis with Incomplete Modalities via Semantic-aware Completeness based Reconstruction](https://mlsanity.com/paper/2609.10950/) (2026)
- [SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data](https://mlsanity.com/paper/2609.30238/) (2026)
- [Reliability-aware Cross-sample Enhancement for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2609.30470/) (2026)
- [Reliability-Aware Multimodal Sentiment Prediction Under Local Modality Missingness](https://mlsanity.com/paper/2610.20543/) (2026)

## Abstract (arXiv)

The field of Multimodal Sentiment Analysis (MSA) has recently witnessed an emerging direction seeking to tackle the issue of data incompleteness. Recognizing that the language modality typically contains dense sentiment information, we consider it as the dominant modality and present an innovative Language-dominated Noise-resistant Learning Network (LNLN) to achieve robust MSA. The proposed LNLN features a dominant modality correction (DMC) module and dominant modality based multimodal learning (DMML) module, which enhances the model's robustness across various noise scenarios by ensuring the quality of dominant modality representations. Aside from the methodical design, we perform comprehensive experiments under random data missing scenarios, utilizing diverse and meaningful settings on several popular datasets (textite.g., MOSI, MOSEI, and SIMS), providing additional uniformity, transparency, and fairness compared to existing evaluations in the literature. Empirically, LNLN consistently outperforms existing baselines, demonstrating superior performance across these challenging and extensive evaluation metrics.