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 201–300 of 853
Wang, X., Lyu, J., Kim, B., Parameshachari, B. D., Li, K., & Li, Q. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3419140 · Cited by 38
Li, M., Zhu, Z., Li, K., & Pei, H. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3430045 · Cited by 33
RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion Recognition
Yuan, L., Huang, G., Li, F., Yuan, X., Pun, C., & Zhong, G. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3284509 · Cited by 31
Structure Aware Multi-Graph Network for Multi-Modal Emotion Recognition in Conversations
Zhang, D., Chen, F., Chang, J., Chen, X., & Tian, Q. C. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3238314 · Cited by 33
Multimodal Sentiment Analysis with Preferential Fusion and Distance-aware Contrastive Learning
Ma, F., Zhang, Y., & Sun, X. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00237 · Cited by 31
Cross-modal contrastive learning for multimodal sentiment recognition
Yang, S., Cui, L., Wang, L., & Wang, T. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05355-8 · Cited by 31
SSLMM: Semi-Supervised Learning with Missing Modalities for Multimodal Sentiment Analysis
Wang, Y., Jian, H., Zhuang, J., Guo, H., & Leng, Y. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103058 · Cited by 34
Deng, H., Yang, Z., Hao, T., Li, Q., & Liu, W. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3211197 · Cited by 32
Multimodal Sentiment Recognition With Multi-Task Learning
Zhang, S., Yin, C., & Yin, Z. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3224929 · Cited by 31
Shen, X., Huang, X., Zou, S., & Gan, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127550 · Cited by 30
UEFN: Efficient uncertainty estimation fusion network for reliable multimodal sentiment analysis
Wang, S., Ratnavelu, K., & Shibghatullah, A. S. (2024) · Applied Intelligence · doi:10.1007/s10489-024-06113-6 · Cited by 30
Transformer-Based Interactive Multi-Modal Attention Network for Video Sentiment Detection
Zhuang, X., Liu, F., Hou, J., Hao, J., & Cai, X. (2022) · Neural Processing Letters · doi:10.1007/s11063-021-10713-5 · Cited by 30
MAG+: An Extended Multimodal Adaptation Gate for Multimodal Sentiment Analysis
Zhao, X., Chen, Y., Li, W., Gao, L., & Tang, B. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746536 · Cited by 30
Hwang, Y., & Kim, J.-H. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.2 · Cited by 31
Text-centered cross-sample fusion network for multimodal sentiment analysis
Huang, Q., Chen, J., Huang, C., Huang, X., & Wang, Y. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01421-w · Cited by 29
Wang, R., Yang, Q., Tian, S., Yu, L., He, X., & Wang, B. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129163 · Cited by 30
Improving Multimodal fusion via Mutual Dependency Maximisation
Colombo, P., Chapuis, É., Labeau, M., & Clavel, C. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.21 · Cited by 36
Zhang, D., Li, S., Zhu, Q., & Zhou, G. (2020) · IEEE Access · doi:10.1109/access.2020.2969205 · Cited by 28
Multimodal Boosting: Addressing Noisy Modalities and Identifying Modality Contribution
Mai, S., Ya, S., Xiong, A., Zeng, Y., & Hu, H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3306489 · Cited by 34
Dia, M., Khodabandelou, G., Sabri, A. Q. M., & Othmani, A. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105712 · Cited by 28
Decoupled cross-attribute correlation network for multimodal sentiment analysis
Zhao, X., Li, X., Jiang, R., & Tang, B. (2025) · Information Fusion · doi:10.1016/j.inffus.2024.102897 · Cited by 31
Modulated Fusion using Transformer for Linguistic-Acoustic Emotion Recognition
Delbrouck, J.-B., Tits, N., & Dupont, S. (2020) · doi:10.18653/v1/2020.nlpbt-1.1 · Cited by 30
Deep-HOSeq: Deep Higher Order Sequence Fusion for Multimodal Sentiment Analysis
Verma, S., Wang, J., Ge, Z., Shen, R., Jin, F., Wang, Y., Chen, F., & Liu, W. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00065 · Cited by 26
Multi-modal Sarcasm Detection Based on Contrastive Attention Mechanism
Zhang, X., Chen, Y., & Li, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88480-2_66 · Cited by 30
Zeng, Y., Mai, S., & Hu, H. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.109 · Cited by 31
Lin, R., & Hu, H. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.36 · Cited by 34
Mai, S., Hu, H., & Xing, S. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3082398 · Cited by 26
A Multimodal Sentiment Analysis Approach Based on a Joint Chained Interactive Attention Mechanism
Qiu, K., Zhang, Y., Zhao, J., Zhang, S., Wang, Q., & Chen, F. (2024) · Electronics · doi:10.3390/electronics13101922 · Cited by 26
Multimodal Decoupled Distillation Graph Neural Network for Emotion Recognition in Conversation
Dai, Y., Li, Y., Chen, D., Li, J., & Lu, G. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3405406 · Cited by 28
Multi-Modal Residual Perceptron Network for Audio–Video Emotion Recognition
Chang, X., & Skarbek, W. (2021) · Sensors · doi:10.3390/s21165452 · Cited by 28
QAP: A Quantum-Inspired Adaptive-Priority-Learning Model for Multimodal Emotion Recognition
Li, Z., Zhou, Y., Liu, Y., Zhu, F., Yang, C., & Hu, S. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.772 · Cited by 26
Quan, Z., Sun, T., Su, M., & Wei, J. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4767437 · Cited by 25
Deep learning on multi-view sequential data: a survey
Xie, Z., Yang, Y. Z., Zhang, Y., Wang, J., & Du, S. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10332-z · Cited by 28
Dai, Y., Li, J., Li, Y., & Lu, G. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111954 · Cited by 27
Sentiment analysis of social media comments based on multimodal attention fusion network
Liu, Z., Yang, T., Chen, W., Chen, J., Li, Q., & Zhang, J. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112011 · Cited by 25
Memory based fusion for multi-modal deep learning
Priyasad, D., Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.10.005 · Cited by 25
Deep multimodal learning for municipal solid waste sorting
Lu, G., Wang, Y., Xu, H., Yang, H., & Zou, J. T. (2021) · Science China Technological Sciences · doi:10.1007/s11431-021-1927-9 · Cited by 25
Li, Y., Yang, Q., Chen, Q., Hu, B., Wang, X., Ding, Y., & Ma, L. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3143324 · Cited by 27
Dynamic Invariant-Specific Representation Fusion Network for Multimodal Sentiment Analysis
He, J., Yanga, H., Zhang, C., Chen, H.-R., & Xua, Y.-S. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2105593 · Cited by 26
Noncontact Multiphysiological Signals Estimation via Visible and Infrared Facial Features Fusion
Ding, S., Ke, Z., Yue, Z., Song, C., & Lu, L. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3209750 · Cited by 25
CMJRT: Cross-Modal Joint Representation Transformer for Multimodal Sentiment Analysis
Xu, M. Y., Liang, F., Su, X., & Fang, C. (2022) · IEEE Access · doi:10.1109/access.2022.3219200 · Cited by 25
UniSA: Unified Generative Framework for Sentiment Analysis
Li, Z., Lin, T.-E., Wu, Y., Liu, M., Tang, F., Zhao, M., & Li, Y. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612336 · Cited by 32
Muti-Modal Emotion Recognition via Hierarchical Knowledge Distillation
Sun, T., Wei, Y., Ni, J., Liu, Z., Song, X., Wang, Y., & Nie, L. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3385180 · Cited by 26
Pu, Y., Gong, M., Liu, T., Zhang, M., Gao, T., Jiang, F., & Hu, X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3480091 · Cited by 26
Multimodal Reaction: Information Modulation for Cross-Modal Representation Learning
Zeng, Y., Mai, S., Yan, W., & Hu, H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3293335 · Cited by 25
A text guided multi-task learning network for multimodal sentiment analysis
Luo, Y., Wu, R., Liu, J., & Tang, X. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126836 · Cited by 25
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)
Augenstein, I., Gella, S., Ruder, S., Kann, K., Can, B., Conneau, A., Welbl, J., Ren, X., & Rei, M. (2019) · doi:10.18653/v1/w19-43 · Cited by 24
Multimodal Sentiment Analysis With Mutual Information-Based Disentangled Representation Learning
Sun, H., Niu, Z., Wang, H., Yu, X., Liu, J., Chen, Y.-W., & Lin, L. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3529732 · Cited by 28
Actual Cause-Guided Adaptive Gradient Scaling for Balanced Multimodal Sentiment Analysis
Chen, J., Huang, Q., Huang, C., & Huang, X. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3736415 · Cited by 24
Two-Level Multimodal Fusion for Sentiment Analysis in Public Security
Sun, J., Yin, H., Tian, Y., Wu, J., Shen, L., & Chen, L. (2021) · Security and Communication Networks · doi:10.1155/2021/6662337 · Cited by 23
Cross-modal dynamic convolution for multi-modal emotion recognition
Wen, H., You, S., & Fu, Y. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103178 · Cited by 23
Multimodal sentiment and emotion recognition in hyperbolic space
Araño, K. A., Orsenigo, C., Soto, M., & Vercellis, C. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115507 · Cited by 23
A Contextual Attention Network for Multimodal Emotion Recognition in Conversation
Wang, T., Hou, Y., Zhou, D., & Zhang, Q. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533718 · Cited by 23
SaliencyBERT: Recurrent Attention Network for Target-Oriented Multimodal Sentiment Classification
Wang, J., Liu, Z., Sheng, V. S., Song, Y., & Qiu, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88010-1_1 · Cited by 23
A graph convolution-based heterogeneous fusion network for multimodal sentiment analysis
Zhao, T., Peng, J., Huang, Y., Wang, L., Zhang, H., & Cai, Z. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05151-w · Cited by 23
Sentiment Knowledge Enhanced Self-supervised Learning for Multimodal Sentiment Analysis
Fan, Q., Han, J., He, Y., Zheng, T., & Zheng, G. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.821 · Cited by 23
Steering Angle-Guided Multimodal Fusion Lane Detection for Autonomous Driving
Gong, Y., Zhang, X., Lu, J., Jiang, X., Wang, Z., Liu, H., Li, Z., Wang, L., Yang, Q., & Wu, X. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3507536 · Cited by 23
Towards robust sentiment analysis with multimodal interaction graph and hybrid contrastive learning
Gong, P., Liu, J., Zhang, X., Li, X., Wei, L., & He, H. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111870 · Cited by 22
Li, X., & Chen, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63031-7_26 · Cited by 22
Correlative Channel-Aware Fusion for Multi-View Time Series Classification
Bai, Y., Wang, L., Tao, Z., Li, S., & Fu, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16830 · Cited by 24
Enhancing emotion inference in conversations with commonsense knowledge
Li, D., Zhu, X., Li, Y., Wang, S., Li, D., Liao, J., & Zheng, J. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107449 · Cited by 23
AMSA: Adaptive Multimodal Learning for Sentiment Analysis
Wang, J., Mou, L., Ма, Л., Huang, T., & Gao, W. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3572915 · Cited by 24
Liu, F., Shen, S.-Y., Fu, Z.-W., Wang, H.-Y., Zhou, A.-M., & Qi, J.-Y. (2022) · Entropy · doi:10.3390/e24071010 · Cited by 21
WavFace: A Multimodal Transformer-Based Model for Depression Screening
Flores, R., Tlachac, M., Shrestha, A., & Rundensteiner, E. A. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3529348 · Cited by 23
CorMulT: A Semi-Supervised Modality Correlation-Aware Multimodal Transformer for Sentiment Analysis
Li, Y., Zhu, R., & Li, W. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3559866 · Cited by 21
Yang, L., Zhong, J., Wen, T., & Liao, Y. J. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103230 · Cited by 21
Pramanick, S., Akhtar, M. S., & Chakraborty, T. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v15i1.18080 · Cited by 21
Deep Tensor Evidence Fusion Network for Sentiment Classification
Wang, Z., Xu, G., Zhou, X., Kim, J., Zhu, H., & Deng, L. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3197994 · Cited by 22
Visual language integration: A survey and open challenges
Park, S., & Kim, Y. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100548 · Cited by 24
Zheng, Y., Gong, J., Wen, Y., & Zhang, P. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122728 · Cited by 23
Hu, R., Yi, J., Chen, A., & Chen, L. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3388670 · Cited by 22
MLGAT: multi-layer graph attention networks for multimodal emotion recognition in conversations
Wu, J., Wu, J., Zheng, Y., Zhan, P., Han, M., Zuo, G., & Yang, L. (2024) · Journal of Intelligent Information Systems · doi:10.1007/s10844-024-00879-4 · Cited by 20
Zhang, P., Fu, M., Zhao, R., Zhang, H., & Luo, C. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3485108 · Cited by 20
Toward Robust Multimodal Sentiment Analysis using multimodal foundational models
Zhao, X., Poria, S., Li, X., Chen, Y., & Tang, B. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126974 · Cited by 28
Self-citationEmotion Detection for Conversations Based on Reinforcement Learning Framework
Huang, X., Ren, M., Han, Q., Shi, X., Nie, J., Nie, W., & Liu, A.-A. (2021) · IEEE Multimedia · doi:10.1109/mmul.2021.3065678 · Cited by 20
Deep Multimodal Sequence Fusion by Regularized Expressive Representation Distillation
Guo, X., Kong, A. W., & Kot, A. C. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3142448 · Cited by 21
Can We Exploit All Datasets? Multimodal Emotion Recognition Using Cross-Modal Translation
Yoon, Y. C. (2022) · IEEE Access · doi:10.1109/access.2022.3183587 · Cited by 23
Hybrid Multimodal Fusion for Humor Detection
Xu, H., Liu, W., Liu, J., Li, M., Feng, Y., Peng, Y., Shi, Y., Sun, X. N., & Wang, M. (2022) · doi:10.1145/3551876.3554802 · Cited by 23
StyleBERT: Text-audio sentiment analysis with Bi-directional Style Enhancement
Lin, F., Liu, S., Zhang, C., Jin, F., & Wu, Z. (2022) · Information Systems · doi:10.1016/j.is.2022.102147 · Cited by 20
Qi, Q., Lin, L., & Zhang, R. (2021) · Information · doi:10.3390/info12090342 · Cited by 19
Multimodal Sentiment Analysis Based on Interactive Transformer and Soft Mapping
Li, Z., Guo, Q., Feng, C., Deng, L., Zhang, Q., Zhang, J., Wang, F., & Sun, Q. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/6243347 · Cited by 18
Hateful Memes Detection Based on Multi-Task Learning
Ma, Z., Yao, S., Wu, L., Gao, S., & Zhang, Y. (2022) · Mathematics · doi:10.3390/math10234525 · Cited by 19
Multi-View Interactive Representations for Multimodal Sentiment Analysis
Tang, Z., Xiao, Q., Qin, Y., Zhou, X., Zhou, J. T., & Li, K. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3357480 · Cited by 20
Chen, R., Zhou, W., Hu, H., Fei, Z., Fei, M., & Zhou, H. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112372 · Cited by 19
VAE-Based Adversarial Multimodal Domain Transfer for Video-Level Sentiment Analysis
Wang, Y., Wu, J., Furumai, K., Wada, S., & Kurihara, S. (2022) · IEEE Access · doi:10.1109/access.2022.3174215 · Cited by 19
Attending to Emotional Narratives
Wu, Z., Zhang, X., Zhi‐Xuan, T., Zaki, J., & Ong, D. C. (2019) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2019.8925497 · Cited by 20
Feature-Enhanced Multimodal Interaction model for emotion recognition in conversation
Fu, Y., Yan, X., Chen, W., & Zhang, J. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112876 · Cited by 19
Blanchard, N., Moreira, D., Bharati, A., & Scheirer, W. J. (2018) · doi:10.18653/v1/w18-3301 · Cited by 18
Deepfake Detection Using Multiple Data Modalities
Hao, H., Bartusiak, E. R., Güera, D., Montserrat, D. M., Baireddy, S., Xiang, Z., Yarlagadda, S. K., Shao, R., Horváth, J., Yang, J., et al. (2022) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-87664-7_11 · Cited by 20
A Multi-Modal ELMo Model for Image Sentiment Recognition of Consumer Data
Rong, L., Ding, Y., Wang, M., Saddik, A. E., & Hossain, M. S. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3357543 · Cited by 18
ArunaGladys, A., & Vetriselvi, V. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111553 · Cited by 36
Joint training strategy of unimodal and multimodal for multimodal sentiment analysis
Meng, L., Zhu, Z., Li, K., Zhou, L., Zhao, Z., & Pei, H. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105172 · Cited by 18
SDRS: Sentiment-Aware Disentangled Representation Shifting for Multimodal Sentiment Analysis
Zhao, S., Yang, Z., Shi, H., Feng, X., Meng, L., Qin, B., Yan, C. C., Tao, J., & Ding, G. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3539225 · Cited by 18
Employing Co-Learning to Evaluate the Explainability of Multimodal Sentiment Analysis
Jain, D. K., Rahate, A., Joshi, G., Walambe, R. A., & Kotecha, K. V. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3176403 · Cited by 17
Cross-Modal Dynamic Transfer Learning for Multimodal Emotion Recognition
Hong, S., Kang, H., & Cho, H. (2024) · IEEE Access · doi:10.1109/access.2024.3356185 · Cited by 20
Meta Noise Adaption Framework for Multimodal Sentiment Analysis With Feature Noise
Yuan, Z., Zhang, B., Xu, H., & Gao, K. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3362600 · Cited by 19
A Three-stage multimodal emotion recognition network based on text low-rank fusion
Zhao, L., Yang, Y., & Ning, T. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01345-5 · Cited by 21
Multimodal sentiment analysis method based on image-text quantum transformer
Wu, H., Kong, D., Wang, L., Li, D., Zhang, J., & Han, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130107 · Cited by 17
MERBench: A Unified Evaluation Benchmark for Multimodal Emotion Recognition
Lian, Z., Sun, L., Ren, Y., Gu, H., Sun, H., Chen, L., Liu, B., & Tao, J. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3653457 · Cited by 71
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
