Cited by 855Multimodal Machine Learning ApplicationsTime Series Analysis and ForecastingTopic Modeling

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Memory Fusion Network for Multi-view Sequential Learning

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Zadeh, Amir, Liang, Paul Pu, Mazumder, Navonil, Poria, Soujanya, Cambria, Erik, Morency, Louis-Philippe·2018·Proceedings of the AAAI Conference on Artificial Intelligence

DOI 10.1609/aaai.v32i1.12021

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Abstract

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.

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Support-vector networks
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-citation
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
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

Show all 36 references

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) .

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He, P., Qi, H., Wang, S., & Cang, J. (2023) · Applied Sciences · doi:10.3390/app13137489 · Cited by 8
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Dumpala, S. H., Sheikh, I., Chakraborty, R., & Kopparapu, S. K. (2018) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639665 · Cited by 7
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Wu, L., Zhang, D., Liu, Q., Li, S., & Zhou, G. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102820 · Cited by 6
Deep Emotion Recognition using Facial, Speech and Textual Cues: A SurveyPreprint
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Guo, X., Tian, S., Yu, L., He, X., & Wang, Z. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108844 · Cited by 5
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Li, Y., Wang, D., Wang, W., Wang, J., & Fang, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109350 · Cited by 5
Evaluation of Data Inconsistency for Multi-modal Sentiment Analysis
Wang, Y., & Wu, M. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-1045-7_25 · Cited by 10
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Koren, L., Stipanĉić, T., Ričko, A., & Orsag, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05061-9_23 · Cited by 5
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Lu, W., & Zhang, D. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859683 · Cited by 5
ER-MRL: Emotion Recognition based on Multimodal Representation Learning
Guo, X., Wang, Y., Miao, Z., Yang, X., Guo, J., Hou, X., & Zao, F. (2022) · International Conference on Information Science and Technology · doi:10.1109/icist55546.2022.9926848 · Cited by 5
Multi-loop graph convolutional network for multimodal conversational emotion recognition
Ren, M., Huang, X., Li, W., & Liu, J. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.103846 · Cited by 5
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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)
ORCID: Zadeh, Amir
Liang, Paul Pu
Poria, Soujanya
Cambria, Erik
Morency, Louis-Philippe
Venue: Proceedings of the AAAI Conference on Artificial Intelligence
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Published: 2018-04-27
Volume: 32
Issue: 1
ISSN: 2159-5399 (ISSN-L)
2374-3468
DOI: 10.1609/aaai.v32i1.12021
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

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