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With the continuous development of deep learning (DL), the task of multimodal dialogue emotion recognition (MDER) has recently received extensive research attention, which is also an essential branch of DL.
C. Busso, M. Bulut, C.-C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J. N. Chang, S. Lee, and S. S. Narayanan, “Iemocap: Interactive emotional dyadic motion capture database,” Language resources and evaluation , vol. 42, no. 4, pp. 335–359, 2008
2008
Earlier work this paper cites.
L. van der Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of Machine Learning Research , vol. 9, no. 86, pp. 2579–2605, 2008
2008
Earlier work this paper cites.
Y. Kim, “Convolutional neural networks for sentence classification,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) . ACL, 2014, pp. 1746–1751
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations , 2015
2015
Earlier work this paper cites.
S. Poria, E. Cambria, D. Hazarika, N. Majumder, A. Zadeh, and L.-P. Morency, “Context-dependent sentiment analysis in user-generated videos,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . ACL, 2017, pp. 873–883
2017
Earlier work this paper cites.
Y. Jia, Y. Zhang, R. Weiss, Q. Wang, J. Shen, F. Ren, z. Chen, P. Nguyen, R. Pang, I. Lopez Moreno, and Y. Wu, “Transfer learning from speaker verification to multispeaker text-to-speech synthesis,” in Advances in Neural Information Processing Systems , vol. 31. MIT, 2018
2018
Earlier work this paper cites.
D. Hazarika, S. Poria, A. Zadeh, E. Cambria, L.-P. Morency, and R. Zimmermann, “Conversational memory network for emotion recognition in dyadic dialogue videos,” in Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting , vol. 2018. NIH Public Access, 2018, p. 2122
2018
Earlier work this paper cites.
Z. Liu, Y. Shen, V. B. Lakshminarasimhan, P. P. Liang, A. Bagher Zadeh, and L.-P. Morency, “Efficient low-rank multimodal fusion with modality-specific factors,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . ACL, 2018, pp. 2247–2256
2018
Earlier work this paper cites.
W. J. Baddar and Y. M. Ro, “Mode variational lstm robust to unseen modes of variation: Application to facial expression recognition,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, pp. 3215–3223, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
S. Poria, D. Hazarika, N. Majumder, G. Naik, E. Cambria, and R. Mihalcea, “MELD: A multimodal multi-party dataset for emotion recognition in conversations,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . ACL, 2019, pp. 527–536
2019
Earlier work this paper cites.
N. Majumder, S. Poria, D. Hazarika, R. Mihalcea, A. Gelbukh, and E. Cambria, “Dialoguernn: An attentive rnn for emotion detection in conversations,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, pp. 6818–6825, 2019
2019
Earlier work this paper cites.
D. Ghosal, N. Majumder, S. Poria, N. Chhaya, and A. Gelbukh, “DialogueGCN: A graph convolutional neural network for emotion recognition in conversation,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . ACL, 2019, pp. 154–164
2019
Earlier work this paper cites.
S. K. Khare and V. Bajaj, “Time–frequency representation and convolutional neural network-based emotion recognition,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 7, pp. 2901–2909, 2020
2020
Earlier work this paper cites.
T. Akilan, Q. J. Wu, A. Safaei, J. Huo, and Y. Yang, “A 3d cnn-lstm-based image-to-image foreground segmentation,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 3, pp. 959–971, 2020
2020
Earlier work this paper cites.
J. Wang, M. Xue, R. Culhane, E. Diao, J. Ding, and V. Tarokh, “Speech emotion recognition with dual-sequence lstm architecture,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2020, pp. 6474–6478
2020
Earlier work this paper cites.
C. Li, Z. Bao, L. Li, and Z. Zhao, “Exploring temporal representations by leveraging attention-based bidirectional lstm-rnns for multi-modal emotion recognition,” Information Processing & Management , vol. 57, no. 3, p. 102185, 2020
2020
Earlier work this paper cites.
J. Huang, J. Tao, B. Liu, Z. Lian, and M. Niu, “Multimodal transformer fusion for continuous emotion recognition,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 3507–3511
2020
Cited alongside, same era.
D. Sheng, D. Wang, Y. Shen, H. Zheng, and H. Liu, “Summarize before aggregate: A global-to-local heterogeneous graph inference network for conversational emotion recognition,” in Proceedings of the 28th International Conference on Computational Linguistics . ICCL, 2020, pp. 4153–4163
2020
Cited alongside, same era.
Y. Kalantidis, M. B. Sariyildiz, N. Pion, P. Weinzaepfel, and D. Larlus, “Hard negative mixing for contrastive learning,” Advances in Neural Information Processing Systems , vol. 33, pp. 21 798–21 809, 2020
2020
Cited alongside, same era.
S. Xing, S. Mai, and H. Hu, “Adapted dynamic memory network for emotion recognition in conversation,” IEEE Transactions on Affective Computing , pp. 1–1, 2020
2020
N. Yin, L. Shen, B. Li, M. Wang, X. Luo, C. Chen, Z. Luo, and X.-S. Hua, “Deal: An unsupervised domain adaptive framework for graph-level classification,” in Proceedings of the 30th ACM International Conference on Multimedia , ser. MM ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 3470–3479
2022
Later among the works it cites.
Y. Shou, T. Meng, W. Ai, S. Yang, and K. Li, “Conversational emotion recognition studies based on graph convolutional neural networks and a dependent syntactic analysis,” Neurocomputing , vol. 501, pp. 629–639, 2022
2022
Later among the works it cites.
Y. Shou, T. Meng, W. Ai, C. Xie, H. Liu, and Y. Wang, “Object detection in medical images based on hierarchical transformer and mask mechanism,” Computational Intelligence and Neuroscience , vol. 2022, 2022
2022
Later among the works it cites.
Z. Hou, X. Liu, Y. Cen, Y. Dong, H. Yang, C. Wang, and J. Tang, “Graphmae: Self-supervised masked graph autoencoders,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . ACM, 2022, p. 594–604
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Cited alongside, same era.
T. Ishiwatari, Y. Yasuda, T. Miyazaki, and J. Goto, “Relation-aware graph attention networks with relational position encodings for emotion recognition in conversations,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2020, pp. 7360–7370
2020
Cited alongside, same era.
R. Ying, Y. Shou, and C. Liu, “Prediction model of dow jones index based on lstm-adaboost,” in 2021 International Conference on Communications, Information System and Computer Engineering (CISCE) . IEEE, 2021, pp. 808–812
2021
Cited alongside, same era.
Z. Lian, B. Liu, and J. Tao, “Ctnet: Conversational transformer network for emotion recognition,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 29, pp. 985–1000, 2021
2021
Cited alongside, same era.
Z. Zhang, P. Cui, J. Pei, X. Wang, and W. Zhu, “Eigen-gnn: a graph structure preserving plug-in for gnns,” IEEE Transactions on Knowledge and Data Engineering , pp. 1–1, 2021
2021
Cited alongside, same era.
S. Zheng, W. Cao, W. Xu, and J. Bian, “Revisiting the evaluation of end-to-end event extraction,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 , 2021, pp. 4609–4617
2021
Cited alongside, same era.
S. Latif, R. Rana, S. Khalifa, R. Jurdak, J. Qadir, and B. W. Schuller, “Survey of deep representation learning for speech emotion recognition,” IEEE Transactions on Affective Computing , pp. 1–1, 2021
2021
Cited alongside, same era.
D. Liu, S. Xu, X.-Y. Liu, Z. Xu, W. Wei, and P. Zhou, “Spatiotemporal graph neural network based mask reconstruction for video object segmentation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 3, 2021, pp. 2100–2108
2021
Cited alongside, same era.
X. Wang, N. Liu, H. Han, and C. Shi, “Self-supervised heterogeneous graph neural network with co-contrastive learning,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . ACM, 2021, p. 1726–1736
2021
Cited alongside, same era.
2022
Later among the works it cites.
W. Kong, M. Qiu, M. Li, X. Jin, and L. Zhu, “Causal graph convolutional neural network for emotion recognition,” IEEE Transactions on Cognitive and Developmental Systems , pp. 1–1, 2022
2022
Later among the works it cites.
D. Cai, S. Qian, Q. Fang, J. Hu, W. Ding, and C. Xu, “Heterogeneous graph contrastive learning network for personalized micro-video recommendation,” IEEE Transactions on Multimedia , 2022
2022
Later among the works it cites.
P. Peng, J. Lu, T. Xie, S. Tao, H. Wang, and H. Zhang, “Open-set fault diagnosis via supervised contrastive learning with negative out-of-distribution data augmentation,” IEEE Transactions on Industrial Informatics , pp. 1–1, 2022
2022
Later among the works it cites.
J. Lee and W. Lee, “Compm: Context modeling with speaker’s pre-trained memory tracking for emotion recognition in conversation,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2022, pp. 5669–5679
2022
Later among the works it cites.
A. Joshi, A. Bhat, A. Jain, A. Singh, and A. Modi, “Cogmen: Contextualized gnn based multimodal emotion recognition,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2022, pp. 4148–4164
2022
Later among the works it cites.
N. Yin, L. Shen, M. Wang, X. Luo, Z. Luo, and D. Tao, “Omg: Towards effective graph classification against label noise,” IEEE Transactions on Knowledge and Data Engineering , vol. 35, no. 12, pp. 12 873–12 886, 2023
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T. Meng, Y. Shou, W. Ai, J. Du, H. Liu, and K. Li, “A multi-message passing framework based on heterogeneous graphs in conversational emotion recognition,” Neurocomputing , p. 127109, 2023
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