MIDAS: Mutual Information Disentanglement with Uncertainty-Aware Fusion for Incomplete Multimodal Sentiment Analysis

arXiv:2608.09986 · cs.AI, cs.LG · Submitted 2026-08-06 · Read on arXiv

Yuhua Wen, Yingying Zhou, Qifei Li, Yingming Gao, Zhengqi Wen, Jianhua Tao, Ya Li

cs.AI, cs.LG

Submitted: 2026-08-06

Updated: 2026-08-12

Comments: Accepted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2026

Code: https://github.com/ultramarineX/MIDAS

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Mutual Information — 39× in this paper · explained in 2 episodes
• Disentanglement — 34× in this paper · explained in 2 episodes
• Robustness — 22× in this paper · explained in 6 episodes
• Representation Learning — 17× in this paper · explained in 3 episodes
• Multimodal Inputs — 14× in this paper · explained in 2 episodes
• Transformer — 12× in this paper · explained in 2 episodes
• Regularization — 11× in this paper · explained in 3 episodes
• Semantic Alignment — 9× in this paper · explained in 6 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Latent Space — 7× in this paper · explained in 7 episodes
• Multimodal Learning — 6× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Distillation — 5× in this paper · explained in 2 episodes
• F1 Score — 5× in this paper · explained in 4 episodes
• Latent Variables — 5× in this paper · explained in 2 episodes
• Multimodal Fusion — 5× in this paper · explained in 4 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Feature Extraction — 4× in this paper · explained in 3 episodes
• Aleatoric Uncertainty — 3× in this paper · explained in 7 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Hyperparameters — 3× in this paper · explained in 2 episodes
• Information Bottleneck — 3× in this paper · explained in 3 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Latent Representations — 3× in this paper · explained in 3 episodes
• Unified Framework — 3× in this paper · explained in 6 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 3 episodes
• BERT Model — 2× in this paper · explained in 2 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Contrastive Learning — 2× in this paper · explained in 9 episodes
• Core — 2× in this paper · explained in 2 episodes
• Decoder — 2× in this paper · explained in 2 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 12 episodes
• Distribution Shifts — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 14 episodes
• Mutual Information (MI) — 2× in this paper · explained in 2 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Self-Distillation — 2× in this paper · explained in 7 episodes
• Semantic Segmentation — 2× in this paper · explained in 3 episodes
• Training Objective — 2× in this paper · explained in 3 episodes
• Transformer Encoder — 2× in this paper · explained in 2 episodes
• Transformers — 2× in this paper · explained in 2 episodes
• Weight Decay — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 9 episodes
• Computational Efficiency — 1× in this paper · explained in 2 episodes
• Data Fusion — 1× in this paper · explained in 3 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 12 episodes
• Evaluation Protocol — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Jensen–Shannon Divergence (JSD) — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 15 episodes
• Mean Absolute Error — 1× in this paper
• Monte Carlo Dropout — 1× in this paper · explained in 3 episodes
• Multimodal Data Fusion — 1× in this paper · explained in 2 episodes
• Multimodal Integration — 1× in this paper · explained in 2 episodes
• Papers — 1× in this paper · explained in 3 episodes
• Parameterization — 1× in this paper · explained in 2 episodes
• Predictive Coding — 1× in this paper · explained in 3 episodes
• Residual Connections — 1× in this paper · explained in 3 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Deep Multimodal Learning with Missing Modality: A Survey
• Deep Variational Information Bottleneck
• On Mutual Information Maximization for Representation Learning
• Representation Learning with Contrastive Predictive Coding
• Multimodal Representations Learning Based on Mutual Information Maximization and Minimization and Identity Embedding for Multimodal Sentiment Analysis
• Auto-Encoding Variational Bayes
• Adam: A Method for Stochastic Optimization

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