The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Memory Fusion Network for Multi-view Sequential Learning
Open accessAbstract
Multi-view sequential learning is a fundamental problem in machine learning dealing with multi-view sequences. In a multi-view sequence, there exists two forms of interactions between different views: view-specific interactions and cross-view interactions. In this paper, we present a new neural architecture for multi-view sequential learning called the Memory Fusion Network (MFN) that explicitly accounts for both interactions in a neural architecture and continuously models them through time. The first component of the MFN is called the System of LSTMs, where view-specific interactions are learned in isolation through assigning an LSTM function to each view. The cross-view interactions are then identified using a special attention mechanism called the Delta-memory Attention Network (DMAN) and summarized through time with a Multi-view Gated Memory. Through extensive experimentation, MFN is compared to various proposed approaches for multi-view sequential learning on multiple publicly available benchmark datasets. MFN outperforms all the multi-view approaches. Furthermore, MFN outperforms all current state-of-the-art models, setting new state-of-the-art results for all three multi-view datasets.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
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
An argument for basic emotions
Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646
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 7,005
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
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
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
Self-citationMultiple 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
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
A Survey on Multi-view LearningPreprint
Xu, C., Tao, D., & Xu, C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1304.5634 · Cited by 1,252
Research works printed but not linked: 3
- Alku, P.; Strik, H.; and Vilkman, E. 1997. Parabolic spectral parametera new method for quantification of the glottal flow. Speech Communication 22(1):67-79.
- Degottex, G.; Kane, J.; Drugman, T.; Raitio, T.; and Scherer, S. 2014. Covarepa collaborative voice analysis repository for speech technologies. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on , 960-964. IEEE.
- Perez-Rosas, V.; Mihalcea, R.; and Morency, L.-P. 2013. UtteranceLevel Multimodal Sentiment Analysis. In Association for Computational Linguistics (ACL) .
Cited by
Sorted by citations
Items 101–200 of 853
Humor Knowledge Enriched Transformer for Understanding Multimodal Humor
Hasan, M. K., Lee, S., Rahman, W., Zadeh, A., Mihalcea, R. F., Morency, L., & Hoque, M. E. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17534 · Cited by 80
Self-citationFan, C., Zhu, K., Tao, J., Yi, G., Xue, J., & Lv, Z. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3423671 · Cited by 81
LR-GCN: Latent Relation-Aware Graph Convolutional Network for Conversational Emotion Recognition
Ren, M., Huang, X., Li, W., Song, D., & Nie, W. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3117062 · Cited by 76
Self-Attention Based Visual-Tactile Fusion Learning for Predicting Grasp Outcomes
Cui, S., Wang, R., Wei, J., Hu, J., & Wang, S. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3010720 · Cited by 72
Multi-Task Momentum Distillation for Multimodal Sentiment Analysis
Lin, R., & Hu, H. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3282410 · Cited by 75
Audio-Visual Event Localization via Recursive Fusion by Joint Co-Attention
Duan, B., Tang, H., Wang, W., Zong, Z., Yang, G., & Yan, Y. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00406 · Cited by 72
MIntRec: A New Dataset for Multimodal Intent Recognition
Zhang, H., Xu, H., Wang, X. Y., Zhou, Q., Zhao, S., & Teng, J. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547906 · Cited by 77
AtCAF: Attention-based causality-aware fusion network for multimodal sentiment analysis
Huang, C., Chen, J., Huang, Q., Wang, S., Tu, Y., & Huang, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102725 · Cited by 76
Fusing pairwise modalities for emotion recognition in conversations
Fan, C., Lin, J., Mao, R., & Cambria, E. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102306 · Cited by 69
Self-citationLu, Q., Sun, X., Gao, Z., Long, Y., Feng, J., & Zhang, H. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103538 · Cited by 69
Multimodal sentiment analysis based on multi-layer feature fusion and multi-task learning
Cai, Y., Li, X., Zhang, Y., Li, J., Zhu, F., & Rao, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-85859-6 · Cited by 68
Multi-Fusion Residual Memory Network for Multimodal Human Sentiment Comprehension
Mai, S., Hu, H., Jia, X., & Xing, S. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3000510 · Cited by 66
I-GCN: Incremental Graph Convolution Network for Conversation Emotion Detection
Nie, W., Chang, R., Ren, M., Su, Y., & Liu, A.-A. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3118881 · Cited by 72
Li, M., Yang, D., Lei, Y., Wang, S., Wang, S., Su, L., Yang, K., Wang, Y., Sun, M., & Zhang, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i9.28871 · Cited by 75
Learning to Balance the Learning Rates Between Various Modalities via Adaptive Tracking Factor
Ya, S., Mai, S., & Hu, H. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3101421 · Cited by 65
Cheng, H., Yang, Z., Zhang, X., & Yang, Y. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3265653 · Cited by 70
Sentiment Analysis: Comprehensive Reviews, Recent Advances, and Open Challenges
Lu, Q., Sun, X., Long, Y., Gao, Z., Feng, J. H., & Sun, T. S. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3294810 · Cited by 77
Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module
Liu, Y., Yuan, Z., Mao, H., Liang, Z., Yang, W., Qiu, Y., Cheng, T., Li, X., Xu, H., & Gao, K. (2022) · Proceedings of the 2022 International Conference on Multimodal Interaction · doi:10.1145/3536221.3556630 · Cited by 64
Ji, X., Dong, Z., Han, Y., Lai, C. S., & Qi, D. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3275708 · Cited by 66
Dynamically Adjust Word Representations Using Unaligned Multimodal Information
Guo, J., Tang, J., Dai, W., Ding, Y., & Kong, W. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548137 · Cited by 65
Survey on multimodal approaches to emotion recognition
Aruna Gladys, A., & Vetriselvi, V. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126693 · Cited by 62
A cognitive brain model for multimodal sentiment analysis based on attention neural networks
Li, Y., Zhang, K., Wang, J., & Gao, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.021 · Cited by 64
Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation
Reza, M. K., Prater-Bennette, A., & Asif, M. S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3476487 · Cited by 78
Quantum-inspired Neural Network for Conversational Emotion Recognition
Li, Q., Gkoumas, D., Sordoni, A., Nie, J., & Melucci, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17567 · Cited by 60
MEDT: Using Multimodal Encoding-Decoding Network as in Transformer for Multimodal Sentiment Analysis
Qi, Q., Lin, L., Zhang, R., & Xue, C. (2022) · IEEE Access · doi:10.1109/access.2022.3157712 · Cited by 66
Masked Graph Learning With Recurrent Alignment for Multimodal Emotion Recognition in Conversation
Meng, T., Zhang, F., Shou, Y., Shao, H., Ai, W., & Li, K. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3434495 · Cited by 60
Deep Multimodal Emotion Recognition on Human Speech: A Review
Koromilas, P., & Giannakopoulos, T. (2021) · Applied Sciences · doi:10.3390/app11177962 · Cited by 58
Learning to Learn Better Unimodal Representations via Adaptive Multimodal Meta-Learning
Ya, S., Mai, S., & Hu, H. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3178231 · Cited by 61
Chen, Q., Huang, G., & Wang, Y. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3192728 · Cited by 58
Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Space
Xie, Z., Yang, Y. Z., Wang, J., Liu, X., & Li, X. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3376564 · Cited by 65
Multimodal Sentiment Analysis Using Multi-tensor Fusion Network with Cross-modal Modeling
Yan, X., Xue, H., Jiang, S., & Liu, Z. (2021) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.2000688 · Cited by 57
Zheng, J., Zhang, S., Wang, Z., Wang, X., & Zeng, Z. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3144885 · Cited by 63
Excavating multimodal correlation for representation learning
Mai, S., Ya, S., Zeng, Y., & Hu, H. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.11.003 · Cited by 57
Zhang, Z., Wang, Z., Li, X., Liu, N., Guo, B., & Yu, Z. (2021) · World Wide Web · doi:10.1007/s11280-021-00955-7 · Cited by 57
Multimodal Emotion Recognition Based on Cascaded Multichannel and Hierarchical Fusion
Liu, X., Xu, Z., & Huang, K. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/9645611 · Cited by 55
A Survey on Multimodal Knowledge Graphs: Construction, Completion and Applications
Chen, Y., Ge, X., Yang, S., Hu, L., Li, J., & Zhang, J. (2023) · Mathematics · doi:10.3390/math11081815 · Cited by 76
Lin, R., & Hu, H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3303711 · Cited by 59
Dynamic hypergraph convolutional network for multimodal sentiment analysis
Huang, J., Pu, Y., Zhou, D., Cao, J., Gu, J., Zhao, Z., & Xu, D. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126992 · Cited by 57
Multimodal sentiment analysis with unimodal label generation and modality decomposition
Zhu, L., Zhao, H., Zhu, Z., Zhang, C., & Kong, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102787 · Cited by 55
LD-MAN: Layout-Driven Multimodal Attention Network for Online News Sentiment Recognition
Guo, W., Zhang, Y., Cai, X., Meng, L., Yang, J., & Yuan, X. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3003648 · Cited by 56
SWAFN: Sentimental Words Aware Fusion Network for Multimodal Sentiment Analysis
Chen, M., & Li, X. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.93 · Cited by 54
Modality-invariant temporal representation learning for multimodal sentiment classification
Sun, H., Liu, J., Chen, Y., & Lin, L. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.10.031 · Cited by 57
CIME: Contextual Interaction-Based Multimodal Emotion Analysis With Enhanced Semantic Information
Wang, R., Guo, C., Cambria, E., Rida, I., Yuan, H., Piran, M. J., Feng, Y., & Zhu, X. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3572495 · Cited by 63
Self-citationModeling Incongruity between Modalities for Multimodal Sarcasm Detection
Wu, Y., Zhao, Y., Lu, X., Qin, B., Wu, Y., Sheng, J., & Li, J. (2021) · IEEE Multimedia · doi:10.1109/mmul.2021.3069097 · Cited by 55
Huan, R., Zhong, G., Chen, P., & Liang, R. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3338769 · Cited by 60
Shou, Y., Liu, H., Cao, X., Meng, D., & Dong, B. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3498443 · Cited by 53
Video multimodal emotion recognition based on Bi-GRU and attention fusion
Huan, R., Shu, J., Bao, S.-L., Liang, R., Chen, P., & Chi, K. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10030-4 · Cited by 52
Video multimodal sentiment analysis using cross-modal feature translation and dynamical propagation
Gan, C., Tang, Y., Fu, X., Zhu, Q., Jain, D. K., & García, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111982 · Cited by 52
DeepCU: Integrating both Common and Unique Latent Information for Multimodal Sentiment Analysis
Verma, S., Wang, C., Zhu, L., & Liu, W. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/503 · Cited by 53
Adaptive Modality Distillation for Separable Multimodal Sentiment Analysis
Peng, W., Hong, X., & Zhao, G. (2021) · IEEE Intelligent Systems · doi:10.1109/mis.2021.3057757 · Cited by 53
Feature Fusion for Multimodal Emotion Recognition Based on Deep Canonical Correlation Analysis
Zhang, K., Li, Y., Wang, J., Wang, Z., & Li, X. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3112314 · Cited by 51
Shared-Private Memory Networks For Multimodal Sentiment Analysis
Zhao, X., Chen, Y., Liu, S., & Tang, B. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3222023 · Cited by 50
Xiao, L., Wu, X., Wu, W., Yang, J., & He, L. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747542 · Cited by 49
Li, X., Liu, J., Xie, Y., Gong, P., Zhang, X., & He, H. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111126 · Cited by 49
Recent advances in deep learning based sentiment analysis
Yuan, J., Wu, Y., Lu, X., Zhao, Y., Qin, B., & Liu, T. (2020) · Science China Technological Sciences · doi:10.1007/s11431-020-1634-3 · Cited by 50
MF-BERT: Multimodal Fusion in Pre-Trained BERT for Sentiment Analysis
He, J., & Hu, H. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3139856 · Cited by 48
Learning discriminative multi-relation representations for multimodal sentiment analysis
Tang, Z., Xiao, Q., Zhou, X., Li, Y., Chen, C., & Li, K. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119125 · Cited by 50
Nguyen, C.-V. T., Mai, T., The, S., Kieu, H.-D., & Le, D.-T. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.937 · Cited by 55
FMSA-SC: A Fine-Grained Multimodal Sentiment Analysis Dataset Based on Stock Comment Videos
Song, L., Chen, S., Meng, Z., Sun, M., & Shang, X. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3363641 · Cited by 49
A multimodal fusion method for sarcasm detection based on late fusion
Ding, N., Tian, S., & Yu, L. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12122-9 · Cited by 49
Tan, T., Wang, D., Shull, P. B., & Halilaj, E. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3189648 · Cited by 46
Hybrid cross-modal interaction learning for multimodal sentiment analysis
Fu, Y., Zhang, Z., Yang, R., & Yao, C. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127201 · Cited by 46
Bi-stream graph learning based multimodal fusion for emotion recognition in conversation
Lu, N., Han, Z., Han, M., & Qian, J. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102272 · Cited by 49
CMU-MOSEAS: A Multimodal Language Dataset for Spanish, Portuguese, German and French
Zadeh, A. B., Cao, Y., Hessner, S., Liang, P. P., Poria, S., & Morency, L. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.141 · Cited by 46
Self-citationA fine-grained modal label-based multi-stage network for multimodal sentiment analysis
Peng, J., Wu, T., Zhang, W., Cheng, F., Tan, S., Yi, F., & Huang, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119721 · Cited by 47
Multimodal Local-Global Ranking Fusion for Emotion Recognition
Liang, P. P., Zadeh, A., & Morency, L. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3243019 · Cited by 47
Self-citationMultimodal Mutual Attention-Based Sentiment Analysis Framework Adapted to Complicated Contexts
He, L., Wang, Z., Wang, L., & Li, F. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3276075 · Cited by 45
Weakening the Dominant Role of Text: CMOSI Dataset and Multimodal Semantic Enhancement Network
Jin, C., Luo, C., Yan, M. G., Zhao, G., Zhang, G., & Zhang, S. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3282953 · Cited by 44
A multimodal fusion emotion recognition method based on multitask learning and attention mechanism
Xie, J., Wang, J., Wang, Q., Yang, D., Gu, J., Tang, Y., & Varatnitski, Y. I. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126649 · Cited by 47
Zhang, W., Yang, G., Zhang, N., Xu, L., Wang, X., Zhang, Y., Zhang, H., Ser, J. D., & de Albuquerque, V. H. C. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.01.009 · Cited by 44
Graph Capsule Aggregation for Unaligned Multimodal Sequences
Wu, J., Mai, S., & Hu, H. (2021) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3462244.3479931 · Cited by 45
Multimodal Relational Tensor Network for Sentiment and Emotion Classification
Sahay, S., Kumar, S. H., Xia, R., Huang, J., & Nachman, L. (2018) · doi:10.18653/v1/w18-3303 · Cited by 46
Zhang, Q., Shi, L., Liu, P., Zhu, Z., & Xu, L. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03343-4 · Cited by 43
RetractedToken-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition
Zhou, Q., Xu, H., Li, H., Zhang, H., Zhang, X., Wang, Y., & Gao, K. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i15.29656 · Cited by 45
Deep Vision Multimodal Learning: Methodology, Benchmark, and Trend
Chai, W., & Wang, G. (2022) · Applied Sciences · doi:10.3390/app12136588 · Cited by 48
VLP2MSA: Expanding vision-language pre-training to multimodal sentiment analysis
Yi, G., Fan, C., Zhu, K., Lv, Z., Liang, S., Wen, Z., Pei, G., Li, T., & Tao, J. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111136 · Cited by 43
EMOE: Modality-Specific Enhanced Dynamic Emotion Experts
Fang, Y., Huang, W., Wan, G., Su, K., & Ye, M. (2025) · 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/cvpr52734.2025.01335 · Cited by 52
Multi-Attention Fusion Network for Video-based Emotion Recognition
Wang, Y., Wu, J., & Hoashi, K. (2019) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3340555.3355720 · Cited by 41
PriMonitor: An adaptive tuning privacy-preserving approach for multimodal emotion detection
Yin, L., Lin, S., Sun, Z., Wang, S., Li, R., & He, Y. (2024) · World Wide Web · doi:10.1007/s11280-024-01246-7 · Cited by 40
Defending Multimodal Fusion Models against Single-Source Adversaries
Yang, K., Lin, W.-Y., Barman, M., Condessa, F., & Kolter, J. Z. (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/cvpr46437.2021.00335 · Cited by 48
Foundations of Multimodal Co-learning
Zadeh, A., Liang, P. P., & Morency, L.-P. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.001 · Cited by 40
Self-citationMemobert: Pre-Training Model with Prompt-Based Learning for Multimodal Emotion Recognition
Zhao, J., Li, R., Jin, Q., Wang, X., & Li, H. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9746910 · Cited by 39
Learning speaker-independent multimodal representation for sentiment analysis
Wang, J., Wang, S., Lin, M., Xu, Z., & Guo, W. (2023) · Information Sciences · doi:10.1016/j.ins.2023.01.116 · Cited by 40
Learning from the global view: Supervised contrastive learning of multimodal representation
Mai, S., Zeng, Y., & Hu, H. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101920 · Cited by 44
Guo, L., Song, Y., & Ding, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111969 · Cited by 39
Wang, Z., Gao, P., & Chu, X. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101588 · Cited by 40
Huang, D., Zhou, S., & Jiang, D. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10016-4 · Cited by 40
Shi, J., Li, M., Chen, Y., Cui, L., & Bai, L. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121393 · Cited by 38
Token-disentangling Mutual Transformer for multimodal emotion recognition
Yin, G., Liu, Y., Liu, T., Zhang, H., Fang, F., Tang, C., & Jiang, L. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108348 · Cited by 43
HMTL: Heterogeneous Modality Transfer Learning for Audio-Visual Sentiment Analysis
Seo, S., Na, S., & Kim, J. (2020) · IEEE Access · doi:10.1109/access.2020.3006563 · Cited by 38
A Unimodal Reinforced Transformer With Time Squeeze Fusion for Multimodal Sentiment Analysis
He, J., Mai, S., & Hu, H. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3078074 · Cited by 37
Survey of deep emotion recognition in dynamic data using facial, speech and textual cues
Zhang, T., & Tan, Z. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17944-9 · Cited by 36
Modality-based Factorization for Multimodal Fusion
Barezi, E. J., & Fung, P. (2019) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w19-4331 · Cited by 39
Kang, P.-Q., Li, J., Jiang, S., & Shull, P. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3232159 · Cited by 36
General Debiasing for Multimodal Sentiment Analysis
Sun, T., Ni, J., Wang, W., Jing, L., Wei, Y., & Nie, L. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612051 · Cited by 37
Adapt and explore: Multimodal mixup for representation learning
Lin, R., & Hu, H. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102216 · Cited by 34
Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis
Gkoumas, D., Li, Q., Dehdashti, S., Melucci, M., Yu, Y., & Song, D. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16165 · Cited by 34
Privacy-Preserving and Secure Industrial Big Data Analytics: A Survey and the Research Framework
Liu, L., Li, J., Lv, J., Wang, J., Zhao, S., & Lu, Q. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3353727 · Cited by 41
Guo, R., Wei, J., Sun, L., Yu, B., Chang, G., Liu, D., Zhang, S., Yao, Z., Xu, M., & Bu, L. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108709 · Cited by 58
Interactive Multimodal Attention Network for Emotion Recognition in Conversation
Ren, M., Huang, X., Shi, X., & Nie, W. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3078698 · Cited by 33
Metadata
- Authors
- Zadeh, A., Liang, P. P., Mazumder, N., Poria, S., Cambria, E., & Morency, L.-P.
- Affiliations
- Carnegie Mellon University (US)
- Instituto Politécnico Nacional (MX)
- Nanyang Technological University (SG)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-27
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2787581402
- Cited by
- 855
- References
- 36
- Citations by year
- 2026: 100 · 2025: 169 · 2024: 163 · 2023: 125 · 2022: 118 · 2021: 87 · 2020: 48 · 2019: 29 · 2018: 16
- FWCI
- 13.57
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- LSTM, attention mechanism, temporal modeling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
