Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion

Long Short-Term Memory

Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997

Earlier work this paper cites.

IEMOCAP: Interactive emotional dyadic motion capture database

Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jeannette N Chang, Sungbok Lee, and Shrikanth S Narayanan. 2008 · 2008

Earlier work this paper cites.

Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014

Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014

Earlier work this paper cites.

Convolutional Neural Networks for Sentence Classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics, 1746–1751

Yoon Kim. 2014 · 2014

Earlier work this paper cites.

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) . 873–883

Soujanya Poria, Erik Cambria, Devamanyu Hazarika, Navonil Majumder, Amir Zadeh, and Louis-Philippe Morency. 2017 · 2017

Earlier work this paper cites.

Tensor Fusion Network for Multimodal Sentiment Analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing . 1103–1114

Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. 2017 · 2017

Earlier work this paper cites.

Icon: Interactive conversational memory network for multimodal emotion detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . 2594–2604

Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, Erik Cambria, and Roger Zimmermann. 2018a · 2018

Earlier work this paper cites.

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, 2122

Devamanyu Hazarika, Soujanya Poria, Amir Zadeh, Erik Cambria, Louis-Philippe Morency, and Roger Zimmermann. 2018b · 2018

Earlier work this paper cites.

Efficient Low-rank Multimodal Fusion with Modality-Specific Factors. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers)

Zhun Liu and Ying Shen. 2018 · 2018

Earlier work this paper cites.

Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 257–266

Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019 · 2019

Earlier work this paper cites.

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) . 154–164

Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, and Alexander Gelbukh. 2019 · 2019

Earlier work this paper cites.

Dialoguernn: An attentive rnn for emotion detection in conversations. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 6818–6825

Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander Gelbukh, and Erik Cambria. 2019 · 2019

Earlier work this paper cites.

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 527–536

Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, and Rada Mihalcea. 2019 · 2019

Earlier work this paper cites.

Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations. 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) . 165–176

Peixiang Zhong, Di Wang, and Chunyan Miao. 2019 · 2019

Earlier work this paper cites.

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) . 7360–7370

Taichi Ishiwatari, Yuki Yasuda, Taro Miyazaki, and Jun Goto. 2020 · 2020

Earlier work this paper cites.

Real-time emotion recognition via attention gated hierarchical memory network. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34. 8002–8009

Wenxiang Jiao, Michael Lyu, and Irwin King. 2020 · 2020

Earlier work this paper cites.

An iterative emotion interaction network for emotion recognition in conversations. In Proceedings of the 28th International Conference on Computational Linguistics . 4078–4088

Xin Lu, Yanyan Zhao, Yang Wu, Yijian Tian, Huipeng Chen, and Bing Qin. 2020 · 2020

Earlier work this paper cites.

Scattering gcn: Overcoming oversmoothness in graph convolutional networks

Yimeng Min, Frederik Wenkel, and Guy Wolf. 2020 · 2020

Earlier work this paper cites.

Adapted dynamic memory network for emotion recognition in conversation

Songlong Xing, Sijie Mai, and Haifeng Hu. 2020 · 2020

Earlier work this paper cites.

Ch-sims: A chinese multimodal sentiment analysis dataset with fine-grained annotation of modality. In Proceedings of the 58th annual meeting of the association for computational linguistics . 3718–3727

Wenmeng Yu, Hua Xu, Fanyang Meng, Yilin Zhu, Yixiao Ma, Jiele Wu, Jiyun Zou, and Kaicheng Yang. 2020 · 2020

Earlier work this paper cites.

Learning what and when to drop: Adaptive multimodal and contextual dynamics for emotion recognition in conversation. In Proceedings of the 29th ACM International Conference on Multimedia . 1064–1073

Feiyu Chen, Zhengxiao Sun, Deqiang Ouyang, Xueliang Liu, and Jie Shao. 2021 · 2021

Earlier work this paper cites.

Context and knowledge enriched transformer framework for emotion recognition in conversations. In 2021 International joint conference on neural networks (IJCNN) . IEEE, 1–8

Soumitra Ghosh, Deeksha Varshney, Asif Ekbal, and Pushpak Bhattacharyya. 2021 · 2021

Cited alongside, same era.

Efficiently Modeling Long Sequences with Structured State Spaces. In International Conference on Learning Representations

Albert Gu, Karan Goel, and Christopher Re. 2021 · 2021

Cited alongside, same era.

MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . 5666–5675

Jingwen Hu, Yuchen Liu, Jinming Zhao, and Qin Jin. 2021 · 2021

Cited alongside, same era.

Emoberta: Speaker-aware emotion recognition in conversation with roberta

Original

Taewoon Kim and Piek Vossen. 2021 · 2021

Cited alongside, same era.

Mamba: Linear-time sequence modeling with selective state spaces

Original

Albert Gu and Tri Dao. 2023 · 2023

Later among the works it cites.

Semantic alignment network for multi-modal emotion recognition

Mixiao Hou, Zheng Zhang, Chang Liu, and Guangming Lu. 2023 · 2023

Later among the works it cites.

Multi-modal fusion network with complementarity and importance for emotion recognition

Shuai Liu, Peng Gao, Yating Li, Weina Fu, and Weiping Ding. 2023 · 2023

Later among the works it cites.

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

Hui Ma, Jian Wang, Hongfei Lin, Bo Zhang, Yijia Zhang, and Bo Xu. 2023 · 2023

Later among the works it cites.

Long Range Language Modeling via Gated State Spaces. In International Conference on Learning Representations

Harsh Mehta, Ankit Gupta, Ashok Cutkosky, and Behnam Neyshabur. 2023 · 2023

Later among the works it cites.