Memory Fusion Network for Multi-view Sequential Learning

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works

Glove: Global Vectors for Word Representation

Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works

An argument for basic emotions

Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646 · see citing works

The random subspace method for constructing decision forests

Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 6,874 · see citing works

IEMOCAP: Interactive emotional dyadic motion capture database

Busso, C., Bulut, M., Lee, C.-C., Kazemzadeh, A., Mower, E., Kim, S., Chang, J. N., Lee, S., & Narayanan, S. S. (2008) · Language Resources and Evaluation · doi:10.1007/s10579-008-9076-6 · Cited by 3,869 · see citing works

Statistical Inference for Probabilistic Functions of Finite State Markov Chains

Baum, L. E., & Petrie, T. (1966) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177699147 · Cited by 2,775 · see citing works

Tensor Fusion Network for Multimodal Sentiment Analysis

Zadeh, A., Chen, M., Poria, S., Cambria, E., & Morency, L.-P. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1115 · Cited by 2,102 · see citing works

Multiple kernel learning, conic duality, and the SMO algorithm

Bach, F. R., Lanckriet, G. R. G., & Jordan, M. I. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015424 · Cited by 1,453 · see citing works

Fast Human Detection Using a Cascade of Histograms of Oriented Gradients

Zhu, Q., Yeh, M.-C., Cheng, K.-T., & Avidan, S. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.119 · Cited by 1,399 · see citing works

A Survey on Multi-view LearningPreprint

Xu, C., Tao, D., & Xu, C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1304.5634 · Cited by 983 · see citing works

Context-Dependent Sentiment Analysis in User-Generated Videos

Poria, S., Cambria, E., Hazarika, D., Majumder, N., Zadeh, A., & Morency, L.-P. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1081 · Cited by 939 · see citing works

Multimodal Sentiment Intensity Analysis in Videos: Facial Gestures and Verbal Messages

Zadeh, A., Zellers, R., Pincus, E., & Morency, L.-P. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.94 · Cited by 852 · see citing works

COVAREP — A collaborative voice analysis repository for speech technologies

Degottex, G., Kane, J., Drugman, T., Raitio, T., & Scherer, S. (2014) · 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2014.6853739 · Cited by 695 · see citing works

Speaker identification on the SCOTUS corpus

Yuan, J., & Liberman, M. (2008) · The Journal of the Acoustical Society of America · doi:10.1121/1.2935783 · Cited by 600 · see citing works

Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis

Poria, S., Cambria, E., & Gelbukh, A. F. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1303 · Cited by 553 · see citing works

Hidden Conditional Random Fields

Quattoni, A., Wang, S., Morency, L., Collins, M., & Darrell, T. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1124 · Cited by 489 · see citing works

Towards multimodal sentiment analysis: harvesting opinions from the web

Morency, L.-P., Mihalcea, R., & Doshi, P. (2011) · Proceedings of the 13th international conference on multimodal interfaces · doi:10.1145/2070481.2070509 · Cited by 470 · see citing works

Automatic, Dimensional and Continuous Emotion Recognition

Gunes, H., & Pantic, M. (2010) · International Journal of Synthetic Emotions · doi:10.4018/jse.2010101605 · Cited by 466 · see citing works

Vocal quality factors: Analysis, synthesis, and perception

Childers, D. G., & Lee, C. K. (1991) · The Journal of the Acoustical Society of America · doi:10.1121/1.402044 · Cited by 461 · see citing works

Youtube movie reviews: Sentiment analysis in an audio-visual context

Wollmer, M., Weninger, F., Knaup, T., Schuller, B., Sun, C., Sagae, K., & Morency, L.-P. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2013.34 · Cited by 403 · see citing works

Latent-Dynamic Discriminative Models for Continuous Gesture Recognition

Morency, L., Quattoni, A., & Darrell, T. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383299 · Cited by 376 · see citing works

Normalized amplitude quotient for parametrization of the glottal flow.

Alku, P., Bäckström, T., & Vilkman, E. (2002) · The Journal of the Acoustical Society of America · doi:10.1121/1.1490365 · Cited by 287 · see citing works

Facial Expression Analysis

(2009) · Encyclopedia of Biometrics · doi:10.1007/978-0-387-73003-5_2171 · Cited by 265 · see citing works

Deep multimodal fusion for persuasiveness prediction

Nojavanasghari, B., Gopinath, D., Koushik, J., Baltrušaitis, T., & Morency, L. (2016) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2993148.2993176 · Cited by 247 · see citing works

Detection of Glottal Closure Instants From Speech Signals: A Quantitative Review

Thomas Drugman, A., Thomas, M. R., Guðnason, J., Naylor, P. A., & Dutoit, T. (2011) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2170835 · Cited by 236 · see citing works

Untitled work

(2013) · Cited by 236 · see citing works

Joint robust voicing detection and pitch estimation based on residual harmonics

Drugman, T., & Alwan, A. A. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-519 · Cited by 228 · see citing works

Select-additive learning: Improving generalization in multimodal sentiment analysis

Wang, H., Meghawat, A., Morency, L., & Xing, E. P. (2017) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2017.8019301 · Cited by 224 · see citing works

Extending Long Short-Term Memory for Multi-View Structured Learning

Rajagopalan, S. S., Morency, L., Baltrušaitis, T., & Goecke, R. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_21 · Cited by 138 · see citing works

Computational Analysis of Persuasiveness in Social Multimedia

Park, S., Shim, H. S., Chatterjee, M., Sagae, K., & Morency, L. (2014) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2663204.2663260 · Cited by 127 · see citing works

Action Recognition by Hierarchical Sequence Summarization

Song, Y., Morency, L., & Davis, R. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.457 · Cited by 97 · see citing works

Wavelet Maxima Dispersion for Breathy to Tense Voice Discrimination

Kane, J., & Gobl, C. (2013) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2013.2245653 · Cited by 85 · see citing works

Multi-view latent variable discriminative models for action recognition

Song, Y., Morency, L., & Davis, R. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247918 · Cited by 81 · see citing works

Glottal wave analysis with pitch synchronous iterative adaptive inverse filtering

Alku, P. (1991) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1991-257 · Cited by 43 · see citing works

Select-Additive Learning: Improving Cross-individual Generalization in Multimodal Sentiment Analysis.Preprint

Wang, H., Meghawat, A., Morency, L., & Xing, E. P. (2016) · arXiv (Cornell University) · Cited by 29 · see citing works

Select-Additive Learning: Improving Generalization in Multimodal Sentiment AnalysisPreprint

Wang, H., Meghawat, A., Morency, L., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.05244 · Cited by 17 · see citing works

Automatic, Dimensional and Continuous Emotion Recognition

Güneş, H., & Pantić, M. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-1595-3.ch005 · Cited by 16 · see citing works

Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph

Bagher Zadeh, A., Liang, P. P., Poria, S., Cambria, E., & Morency, L.-P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1208 · Cited by 1,433 · see citing works

Efficient Low-rank Multimodal Fusion With Modality-Specific Factors

Liu, Z., Shen, Y., Lakshminarasimhan, V. B., Liang, P. P., Bagher Zadeh, A., & Morency, L.-P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1209 · Cited by 1,170 · see citing works

MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis

Hazarika, D., Zimmermann, R., & Poria, S. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413678 · Cited by 1,073 · see citing works

Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis

Yu, W., Xu, H., Yuan, Z., & Wu, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17289 · Cited by 816 · see citing works

Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions

Gandhi, A., Adhvaryu, K., Poria, S., Cambria, E., & Hussain, A. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.09.025 · Cited by 783 · see citing works

Multimodal deep learning for biomedical data fusion: a review

Stahlschmidt, S. R., Ulfenborg, B., & Synnergren, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab569 · Cited by 766 · see citing works

Integrating Multimodal Information in Large Pretrained Transformers

Rahman, W., Hasan, M. K., Lee, S., Zadeh, A. B., Mao, C., Morency, L., & Hoque, M. E. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.214 · Cited by 743 · see citing works

Harnessing multimodal data integration to advance precision oncology

Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-021-00408-3 · Cited by 662 · see citing works

Deep Multimodal Representation Learning: A Survey

Guo, W., Wang, J., & Wang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2916887 · Cited by 535 · see citing works

Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors

Wang, Y., Shen, Y., Liu, Z., Liang, P. P., Zadeh, A., & Morency, L. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017216 · Cited by 493 · see citing works

ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection

Hazarika, D., Poria, S., Mihalcea, R. F., Cambria, E., & Zimmermann, R. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1280 · Cited by 465 · see citing works

Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition

Liu, W., Qiu, J., Zheng, W., & Lu, B. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3071170 · Cited by 460 · see citing works

CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality

Yu, W., Xu, H., Meng, F., Zhu, Y., Ma, Y., Wu, J., Zou, J., & Yang, K. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.343 · Cited by 482 · see citing works

Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis

Sun, Z., Sarma, P., Sethares, W., & Liang, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6431 · Cited by 410 · see citing works

Multimodal sentiment analysis based on fusion methods: A survey

Zhu, L., Zhu, Z., Zhang, C., Xu, Y., & Kong, X. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.02.028 · Cited by 368 · see citing works

MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation

Hu, J., Liu, Y., Zhao, J., & Jin, Q. (2021) · doi:10.18653/v1/2021.acl-long.440 · Cited by 340 · see citing works

TETFN: A text enhanced transformer fusion network for multimodal sentiment analysis

Wang, D., Guo, X., Tian, Y., Liu, J., He, L., & Luo, X. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109259 · Cited by 349 · see citing works

Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research

Poria, S., Hazarika, D., Majumder, N., & Mihalcea, R. F. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3038167 · Cited by 344 · see citing works

Image–text sentiment analysis via deep multimodal attentive fusion

Huang, F., Zhang, X., Zhao, Z., Xu, J. J., & Li, Z. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.01.019 · Cited by 301 · see citing works

CTNet: Conversational Transformer Network for Emotion Recognition

Lian, Z., Liu, B., & Tao, J. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3049898 · Cited by 300 · see citing works

Attention, please! A survey of neural attention models in deep learning

Correia, A. D. S., & Colombini, E. L. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10148-x · Cited by 330 · see citing works

Adapting BERT for Target-Oriented Multimodal Sentiment Classification

Yu, J., & Jiang, J. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/751 · Cited by 295 · see citing works

Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis

Mai, S., Zeng, Y., Zheng, S., & Hu, H. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3172360 · Cited by 301 · see citing works

Efficient Multimodal Transformer With Dual-Level Feature Restoration for Robust Multimodal Sentiment Analysis

Sun, L., Lian, Z., Liu, B., & Tao, J. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3274829 · Cited by 283 · see citing works

MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversations

Hu, D., Hou, X., Wei, L., Jiang, L., & Mo, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747397 · Cited by 267 · see citing works

M3ER: Multiplicative Multimodal Emotion Recognition using Facial, Textual, and Speech Cues

Mittal, T., Bhattacharya, U., Chandra, R., Bera, A., & Manocha, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i02.5492 · Cited by 23 · see citing works

Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusion

Mai, S., Hu, H., & Xing, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5347 · Cited by 237 · see citing works

Entity-Sensitive Attention and Fusion Network for Entity-Level Multimodal Sentiment Classification

Yu, J., Jiang, J., & Xia, R. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2957872 · Cited by 235 · see citing works

Multimodal Language Analysis with Recurrent Multistage Fusion

Liang, P. P., Liu, Z., Bagher Zadeh, A., & Morency, L.-P. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1014 · Cited by 230 · see citing works

Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis

Akhtar, M. S., Chauhan, D. S., Ghosal, D., Poria, S., Ekbal, A., & Bhattacharyya, P. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1034 · Cited by 233 · see citing works

Modeling both Context- and Speaker-Sensitive Dependence for Emotion Detection in Multi-speaker Conversations

Zhang, D., Wu, L., Sun, C., Li, S., Zhu, Q., & Zhou, G. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/752 · Cited by 230 · see citing works

Decoupled Multimodal Distilling for Emotion Recognition

Li, Y., Wang, Y., & Cui, Z. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00641 · Cited by 249 · see citing works

Progressive Modality Reinforcement for Human Multimodal Emotion Recognition from Unaligned Multimodal Sequences

Lv, F., Chen, X., Huang, Y., Duan, L., & Lin, G. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00258 · Cited by 221 · see citing works

Transformer-based Feature Reconstruction Network for Robust Multimodal Sentiment Analysis

Yuan, Z., Li, W., Xu, H., & Yu, W. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475585 · Cited by 211 · see citing works

A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face

Lian, H., Lu, C., Li, S., Zhao, Y., Tang, C., & Zong, Y. (2023) · Entropy · doi:10.3390/e25101440 · Cited by 224 · see citing works

A Review on Explainability in Multimodal Deep Neural Nets

Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204 · see citing works

Foundations & Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Liang, P. P., Zadeh, A., & Morency, L. (2024) · ACM Computing Surveys · doi:10.1145/3656580 · Cited by 306 · see citing works

ConFEDE: Contrastive Feature Decomposition for Multimodal Sentiment Analysis

Yang, J., Yu, Y., Niu, D., Guo, W., & Xu, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.421 · Cited by 189 · see citing works

Real-Time Video Emotion Recognition Based on Reinforcement Learning and Domain Knowledge

Zhang, K., Li, Y., Wang, J., Cambria, E., & Li, X. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3072412 · Cited by 184 · see citing works

A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations

Ma, H., Wang, J., Lin, H., Zhang, B., Zhang, Y., & Xu, B. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3271019 · Cited by 188 · see citing works

Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations

Mai, S., Zeng, Y., & Hu, H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3171679 · Cited by 182 · see citing works

State of the art: a review of sentiment analysis based on sequential transfer learning

Chan, J. Y.-L., Bea, K. T., Leow, S. M. H., Phoong, S. W., & Cheng, W. K. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10183-8 · Cited by 187 · see citing works

CubeMLP: An MLP-based Model for Multimodal Sentiment Analysis and Depression Estimation

Sun, H., Wang, H., Liu, J., Chen, Y., & Lin, L. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548025 · Cited by 176 · see citing works

UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

Hasan, M. K., Rahman, W., Bagher Zadeh, A., Zhong, J., Tanveer, M. I., Morency, L.-P., & Hoque, M. (. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1211 · Cited by 174 · see citing works

TMBL: Transformer-based multimodal binding learning model for multimodal sentiment analysis

Huang, J., Zhou, J. W., Tang, Z., Lin, J., & Chen, C. Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111346 · Cited by 162 · see citing works

COGMEN: COntextualized GNN based Multimodal Emotion recognitioN

Joshi, A., Bhat, A., Jain, A., Singh, A., & Modi, A. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.306 · Cited by 161 · see citing works

Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches

Khoo, L. S., McNaney, R., Lim, M. K., & McNaney, R. (2024) · Sensors · doi:10.3390/s24020348 · Cited by 178 · see citing works

Divide, Conquer and Combine: Hierarchical Feature Fusion Network with Local and Global Perspectives for Multimodal Affective Computing

Mai, S., Hu, H., & Xing, S. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1046 · Cited by 153 · see citing works

AOBERT: All-modalities-in-One BERT for multimodal sentiment analysis

Kim, K.-H., & Park, S. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.11.022 · Cited by 148 · see citing works

Multimodal Video Sentiment Analysis Using Deep Learning Approaches, a Survey

Abdu, S. A., Yousef, A. H., & Salem, A. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.06.003 · Cited by 147 · see citing works

Quantum-inspired multimodal fusion for video sentiment analysis

Li, Q., Gkoumas, D., Lioma, C., & Melucci, M. (2021) · Information Fusion · doi:10.1016/j.inffus.2020.08.006 · Cited by 143 · see citing works

Memory-Guided Multi-View Multi-Domain Fake News Detection

Zhu, Y., Sheng, Q., Cao, J., Nan, Q., Shu, K., Wu, M., Wang, J., & Zhuang, F. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3185151 · Cited by 145 · see citing works

Transformer Encoder With Multi-Modal Multi-Head Attention for Continuous Affect Recognition

Chen, H., Jiang, D., & Hichem Sahli, B. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3037496 · Cited by 142 · see citing works

Video sentiment analysis with bimodal information-augmented multi-head attention

Wu, T., Peng, J., Zhang, W., Zhang, H., Tan, S., Yi, F., Ma, C., & Huang, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107676 · Cited by 137 · see citing works

A Review of Key Technologies for Emotion Analysis Using Multimodal Information

Zhu, X., Guo, C., Feng, H., Huang, Y., Feng, Y., Wang, X., & Wang, R. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10287-z · Cited by 137 · see citing works

Multilogue-Net: A Context-Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation

Shenoy, A., & Sardana, A. (2020) · doi:10.18653/v1/2020.challengehml-1.3 · Cited by 124 · see citing works

Gated Mechanism for Attention Based Multi Modal Sentiment Analysis

Kumar, A., & Vepa, J. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053012 · Cited by 123 · see citing works

GraphMFT: A graph network based multimodal fusion technique for emotion recognition in conversation

Li, J., Wang, X., Lv, G., & Zeng, Z. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126427 · Cited by 120 · see citing works

A Text-Centered Shared-Private Framework via Cross-Modal Prediction for Multimodal Sentiment Analysis

Wu, Y., Lin, Z., Zhao, Y., Qin, B., & Zhu, L.-N. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.417 · Cited by 129 · see citing works

GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion Detection

Li, J., Wang, X., Lv, G., & Zeng, Z. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3261279 · Cited by 115 · see citing works

A cross modal hierarchical fusion multimodal sentiment analysis method based on multi-task learning

Wang, L., Peng, J., Zheng, C., Zhao, T., & Zhu, L. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103675 · Cited by 111 · see citing works

Emotion recognition based on brain-like multimodal hierarchical perception

Zhu, X., Huang, Y., Wang, X., & Wang, R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17347-w · Cited by 115 · see citing works

Transformer-based Multimodal Information Fusion for Facial Expression Analysis

Zhang, W., Qiu, F., Wang, S., Zeng, H., Zhang, Z., An, R., Ma, B., & Ding, Y. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00271 · Cited by 109 · see citing works

HGMF: Heterogeneous Graph-based Fusion for Multimodal Data with Incompleteness

Chen, J., & Zhang, A. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403182 · Cited by 104 · see citing works

Dynamic interactive multiview memory network for emotion recognition in conversation

Wen, J., Jiang, D., Tu, G., Liu, C., & Cambria, E. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.10.009 · Cited by 102 · see citing works

Multimodal sentiment analysis with unidirectional modality translation

Yang, B., Shao, B., Wu, L., & Lin, X. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.09.041 · Cited by 100 · see citing works

M3Care: Learning with Missing Modalities in Multimodal Healthcare Data

Zhang, C., Chu, X., Ma, L., Zhu, Y., Wang, Y., Wang, J., & Zhao, J. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539388 · Cited by 102 · see citing works

Sentiment analysis in textual, visual and multimodal inputs using recurrent neural networks

Tembhurne, J. V., & Diwan, T. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10037-x · Cited by 98 · see citing works

Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis

Chauhan, D. S., Akhtar, M. S., Ekbal, A., & Bhattacharyya, P. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1566 · Cited by 97 · see citing works

Social-IQ: A Question Answering Benchmark for Artificial Social Intelligence

Zadeh, A., Chan, M., Liang, P. P., Tong, E., & Morency, L. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00901 · Cited by 98 · see citing works

AF: An Association-Based Fusion Method for Multi-Modal Classification

Liang, X., Qian, Y., Guo, Q. J., Cheng, H., & Liang, J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3125995 · Cited by 98 · see citing works

M-SENA: An Integrated Platform for Multimodal Sentiment Analysis

Mao, H., Yuan, Z., Xu, H., Yu, W., Liu, Y., & Gao, K. (2022) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2022.acl-demo.20 · Cited by 108 · see citing works

A Survey of Cutting-edge Multimodal Sentiment Analysis

Singh, U., Abhishek, K., & Azad, H. K. (2024) · ACM Computing Surveys · doi:10.1145/3652149 · Cited by 99 · see citing works

M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis

Wang, X., He, J., Jin, Z., Yang, M., Wang, Y., & Qu, H. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114794 · Cited by 6 · see citing works

Multimodal End-to-End Sparse Model for Emotion Recognition

Dai, W., Cahyawijaya, S., Liu, Z., & Fung, P. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.417 · Cited by 91 · see citing works

CVTNet: A Cross-View Transformer Network for LiDAR-Based Place Recognition in Autonomous Driving Environments

Ma, J., Xiong, G., Xu, J., & Chen, X. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3313635 · Cited by 91 · see citing works

What makes the difference? An empirical comparison of fusion strategies for multimodal language analysis

Gkoumas, D., Li, Q., Lioma, C., Yu, Y., & Song, D. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.09.005 · Cited by 94 · see citing works

SMIN: Semi-Supervised Multi-Modal Interaction Network for Conversational Emotion Recognition

Lian, Z., Liu, B., & Tao, J. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3141237 · Cited by 89 · see citing works

Locally Confined Modality Fusion Network With a Global Perspective for Multimodal Human Affective Computing

Mai, S., Xing, S., & Hu, H. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2925966 · Cited by 89 · see citing works

Analyzing Multimodal Sentiment Via Acoustic- and Visual-LSTM With Channel-Aware Temporal Convolution Network

Mai, S., Xing, S., & Hu, H. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3068598 · Cited by 89 · see citing works

Multimodal Emotion Recognition Fusion Analysis Adapting BERT With Heterogeneous Feature Unification

Lee, S., Han, D. K., & Ko, H. (2021) · IEEE Access · doi:10.1109/access.2021.3092735 · Cited by 91 · see citing works

BAFN: Bi-Direction Attention Based Fusion Network for Multimodal Sentiment Analysis

Tang, J., Liu, D., Jin, X., Peng, Y. K., Zhao, Q., Ding, Y., & Kong, W. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3218018 · Cited by 90 · see citing works

TeFNA: Text-centered fusion network with crossmodal attention for multimodal sentiment analysis

Huang, C., Zhang, J., Wu, X., Wang, Y., Li, M., & Huang, X. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110502 · Cited by 89 · see citing works

Adapted Dynamic Memory Network for Emotion Recognition in Conversation

Xing, S., Mai, S., & Hu, H. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3005660 · Cited by 87 · see citing works

DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

Majumder, N., Poria, S., Hazarika, D., Mihalcea, R. F., Gelbukh, A. F., & Cambria, E. (2019) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v33i01.33016818 · Cited by 901 · see citing works

Multi-Modal Sarcasm Detection and Humor Classification in Code-Mixed Conversations

Bedi, M., Kumar, S., Akhtar, M. S., & Chakraborty, T. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3083522 · Cited by 89 · see citing works

MissModal: Increasing Robustness to Missing Modality in Multimodal Sentiment Analysis

Lin, R., & Hu, H. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00628 · Cited by 91 · see citing works

CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network

Tang, J., Li, K., Jin, X., Cichocki, A. S., Zhao, Q., & Kong, W. (2021) · doi:10.18653/v1/2021.acl-long.412 · Cited by 87 · see citing works

PS-Mixer: A Polar-Vector and Strength-Vector Mixer Model for Multimodal Sentiment Analysis

Lin, H., Zhang, P., Ling, J., Yang, Z., Lee, L., & Liu, W. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103229 · Cited by 84 · see citing works

Deep Emotional Arousal Network for Multimodal Sentiment Analysis and Emotion Recognition

Zhang, F., Li, X., Lim, C. P., Hua, Q., Dong, C.-R., & Zhai, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.07.006 · Cited by 81 · see citing works

Efficient Low-rank Multimodal Fusion with Modality-Specific FactorsPreprint

Liu, Z., Shen, Y., Lakshminarasimhan, V., Liang, P. P., Zadeh, A., & Morency, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.00064 · Cited by 80 · see citing works

Tailor Versatile Multi-Modal Learning for Multi-Label Emotion Recognition

Zhang, Y., Chen, M., Shen, J., & Wang, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20895 · Cited by 79 · see citing works

GPT-4V with emotion: A zero-shot benchmark for Generalized Emotion Recognition

Lian, Z., Sun, L., Sun, H., Chen, K., Wen, Z., Gu, H., Liu, B., & Tao, J. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102367 · Cited by 82 · see citing works

Multi-Modal Sequence Fusion via Recursive Attention for Emotion Recognition

Beard, R., Das, R., Ng, R. W. M., Gopalakrishnan, P. G. K., Eerens, L., Świętojański, P., & Mikšík, O. (2018) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k18-1025 · Cited by 77 · see citing works

Multimodal Sentiment Analysis With Two-Phase Multi-Task Learning

Yang, B., Wu, L., Zhu, J., Shao, B., Lin, X., & Liu, T. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3178204 · Cited by 77 · see citing works

Multimodal emotion recognition based on audio and text by using hybrid attention networks

Zhang, S., Yang, Y., Chen, C., Liu, R., Tao, X., Guo, W., Xu, Y., & Zhao, X. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105052 · Cited by 76 · see citing works

Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis

Pham, H., Manzini, T., Liang, P. P., & Póczos, B. (2018) · doi:10.18653/v1/w18-3308 · Cited by 76 · see citing works

Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection

Pramanick, S., Roy, A., & Johns, V. M. P. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00062 · Cited by 78 · see citing works

TensorFormer: A Tensor-Based Multimodal Transformer for Multimodal Sentiment Analysis and Depression Detection

Sun, H., Chen, Y., & Lin, L. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3233070 · Cited by 77 · see citing works

Attention-based Audio-Visual Fusion for Robust Automatic Speech Recognition

Sterpu, G., Saam, C., & Harte, N. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3243014 · Cited by 74 · see citing works

Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning

Lê, H., Tran, T., & Venkatesh, S. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219981 · Cited by 121 · see citing works

Attention and Memory-Augmented Networks for Dual-View Sequential Learning

He, Y., Wang, C., Li, N., & Zeng, Z. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403055 · Cited by 41 · see citing works

Deep Multiview Learning From Sequentially Unaligned Data

Doan, T., & Takasu, A. (2020) · IEEE Access · doi:10.1109/access.2020.3042217 · Cited by 5 · see citing works

Extending Long Short-Term Memory for Multi-View Structured Learning

Rajagopalan, S. S., Morency, L., Baltrušaitis, T., & Goecke, R. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_21 · Cited by 138 · see citing works

Sequential attention layer-wise fusion network for multi-view classification

Teng, Q., Yang, X., Sun, Q., Wang, P., Wang, X., & Xu, T. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02260-x · Cited by 9 · see citing works