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BNF: Bayesian Network Fusion for Multimodal Sentiment Analysis

Sun, C. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6603-4_6

DMF:A plugin based on dynamic multi-scale fusion for multimodal sentiment analysis

Wu, J. Y., & Chen, W. (2025) · International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR) · doi:10.1109/aihcir67580.2025.11405002

Adaptive Multimodal Semantic Balancing Framework for Sentiment Analysis

Tang, J., Zhou, F., Wang, X., Zhang, J., Zhao, Q., Ding, Y., & Kong, W. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3654415

Robust Multimodal Emotion Recognition from Incomplete Modalities via Query-Based Unimodal and Cross-Modal Learning

Miyoshi, R., Otani, M., & Okafuji, Y. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00476

MEERA: Multimodal experts for evidence routing and sparse LLM adaptation in sentiment analysis

Niu, F., Liao, Y., Peng, X., Huang, H., & Zhang, B. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113922

DCG-Trans: Dual-Consistency Guided Transformer with Uncertainty-Guided Refinement for Robust Multimodal Sentiment Regression

Huang, S., & Thiengburanathum, P. (2026) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp69961.2026.11540775

ReNoRD: Learning from Relations under Noisy Pseudo Labels via Relational Distillation for Multimodal Sentiment

Zhai, T., Yan, Z., Ren, F., Luo, L., & Luo, L. (2026) · International Conference on Multimedia Retrieval · doi:10.1145/3805622.3810693

Emotion Detection Using Multimodal Fusion with Audio, Text, and Facial Expressions in a RealTime Web Application

Jadhav, R. S., Patil, S., Sharma, P., Chougule, S., & Ghule, S. (2026) · International Conference on Computing, Communication and Green Engineering (CCGE) · doi:10.1109/ccge67142.2026.11581725

Multi-level global and local fusion for multimodal sentiment analysis

Zhang, B., Wang, J., Sun, B., & Ma, N. (2026) · Memetic Computing · doi:10.1007/s12293-026-00520-7

COTEA: capsule-optimized tri-modal expert alignment for multimodal sentiment analysis

Liao, Z., Qi, K., Yuan, F., & Liu, J. (2026) · Complex & Intelligent Systems · doi:10.1007/s40747-026-02425-x

GraphDREAM: graph-based decoupled representation and evolution-assisted multi-task network for multimodal emotion recognition

Wu, Y., Hu, R., Li, Y., Luo, Z., Pan, L., & Peng, L. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02592-4

A Lightweight Late Fusion-Based Framework for Temporal Multimodal Emotion Recognition

Ke, C.-Y., & Liang, Y.-L. (2026) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-taiwan71481.2026.11652549

Robust triple disentangled representation learning for multimodal sentiment analysis

Lu, Y., He, Q., & Du, N. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116927

Emotion Emerges from Shared Latent Context

Yang, Z., Pei, G., & Fu, X. (2026) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc70586.2026.11686507

Rethinking Attention Reallocation for Multimodal Emotion Recognition

Lyu, Y., Zou, Y., Hu, J., Li, Y., & Li, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37189-8_1

Adaptive multimodal prompt learning: Dynamic handling of missing modalities for robust emotion recognition

Li, J., Gong, J., Chen, J., Chen, R., Wang, Y., Fu, Y., & Zhao, Y. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.117118

SABTM: Semi-Supervised Multimodal Emotion Recognition via Adaptive Barlow Twins and Mixture-of-Experts

Zhai, T., Luo, L., Yan, Z., & Ren, F. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729987

AVMFAC: Acoustic Visual Multi-scale Feature Aggregation Conformer for Multimodal Sentiment Analysis

Zheng, Q. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8565-4_23 · Cited by 2 · see citing works

Boosting Modality Representation With Pre-Trained Models and Multi-Task Training for Multimodal Sentiment Analysis

Hai, J., Liu, Y.-J., & Elhilali, M. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389694

Multi-Task Non-Verbal Information Enhancement and Cross-Modal Transformer Multi-Level Interactive Fusion for Multimodal Sentiment Analysis

Liu, X., Ma, Z., Li, M., Pu, T., & Wang, G. (2025) · International Conference on Computer Vision and Data Mining (ICCVDM) · doi:10.1109/iccvdm66874.2025.11290084

MIME: Mutual information-guided mixture of experts for robust multimodal sentiment analysis

Zhang, D., Xiao, Q., He, X., & Zhang, J. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00587-9 · Cited by 1 · see citing works

Cross-Modal Enhancement Network for Multimodal Sentiment Analysis

Wang, D., Liu, S., Wang, Q., Tian, Y., He, L., & Gao, X. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3183830 · Cited by 191 · see citing works