Fetching the paper…
Reading the bibliography…
The development of multimodal models has significantly advanced multimodal sentiment analysis and emotion recognition.
Iemocap: interactive emotional dyadic motion capture database
Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Ebrahim (Abe) Kazemzadeh, Emily Mower Provost, Samuel Kim, Jeannette N. Chang, Sungbok Lee, and Shrikanth S. Narayanan. 2008 · 2008
Earlier work this paper cites.
Multimodal sentiment intensity analysis in videos: Facial gestures and verbal messages
Amir Zadeh, Rowan Zellers, Eli Pincus, and Louis-Philippe Morency. 2016 · 2016
Earlier work this paper cites.
Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph
AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. 2018 · 2018
Earlier work this paper cites.
Deep adversarial learning for multi-modality missing data completion
Lei Cai, Zhengyang Wang, Hongyang Gao, Dinggang Shen, and Shuiwang Ji. 2018 · 2018
Earlier work this paper cites.
Semi-supervised deep generative modelling of incomplete multi-modality emotional data
Changde Du, Changying Du, Hao Wang, Jinpeng Li, Wei-Long Zheng, Bao-Liang Lu, and Huiguang He. 2018 · 2018
Earlier work this paper cites.
Multimodal language analysis with recurrent multistage fusion
Paul Pu Liang, Ziyin Liu, AmirAli Bagher Zadeh, and Louis-Philippe Morency. 2018 · 2018
Earlier work this paper cites.
Multimodal and multi-view models for emotion recognition
Gustavo Aguilar, Viktor Rozgic, Weiran Wang, and Chao Wang. 2019 · 2019
Earlier work this paper cites.
Divide, conquer and combine: Hierarchical feature fusion network with local and global perspectives for multimodal affective computing
Sijie Mai, Haifeng Hu, and Songlong Xing. 2019 · 2019
Earlier work this paper cites.
Found in translation: Learning robust joint representations by cyclic translations between modalities
Hai Pham, Paul Pu Liang, Thomas Manzini, Louis-Philippe Morency, and Barnabás Póczos. 2019 · 2019
Earlier work this paper cites.
Multimodal transformer for unaligned multimodal language sequences
Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J. Zico Kolter, Louis-Philippe Morency, and Ruslan Salakhutdinov. 2019 · 2019
Earlier work this paper cites.
Words can shift: Dynamically adjusting word representations using nonverbal behaviors
Yansen Wang, Ying Shen, Zhun Liu, Paul Pu Liang, Amir Zadeh, and Louis-Philippe Morency. 2019 · 2019
Cited alongside, same era.
Misa: Modality-invariant and-specific representations for multimodal sentiment analysis
Devamanyu Hazarika, Roger Zimmermann, and Soujanya Poria. 2020 · 2020
Cited alongside, same era.
CH-SIMS: A Chinese multimodal sentiment analysis dataset with fine-grained annotation of modality
Wenmeng Yu, Hua Xu, Fanyang Meng, Yilin Zhu, Yixiao Ma, Jiele Wu, Jiyun Zou, and Kaicheng Yang. 2020 · 2020
Cited alongside, same era.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
Cited alongside, same era.
Improving multimodal fusion with hierarchical mutual information maximization for multimodal sentiment analysis
Wei Han, Hui Chen, and Soujanya Poria. 2021 · 2021
Cited alongside, same era.
Smil: Multimodal learning with severely missing modality
Mengmeng Ma, Jian Ren, Long Zhao, Sergey Tulyakov, Cathy Wu, and Xi Peng. 2021 · 2021
Later among the works it cites.
On the stability of fine-tuning {bert}: Misconceptions, explanations, and strong baselines
Marius Mosbach, Maksym Andriushchenko, and Dietrich Klakow. 2021 · 2021
Later among the works it cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021 · 2021
Later among the works it cites.
Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill. 2021 · 2021
Later among the works it cites.
Missing modality imagination network for emotion recognition with uncertain missing modalities
Jinming Zhao, Ruichen Li, and Qin Jin. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Language models as knowledge bases: On entity representations, storage capacity, and paraphrased queries
Benjamin Heinzerling and Kentaro Inui. 2021 · 2021
Cited alongside, same era.
Vilt: Vision-and-language transformer without convolution or region supervision
Wonjae Kim, Bokyung Son, and Ildoo Kim. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
Align before fuse: Vision and language representation learning with momentum distillation
Junnan Li, Ramprasaath R. Selvaraju, Akhilesh Deepak Gotmare, Shafiq Joty, Caiming Xiong, and Steven Hoi. 2021 · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
UniMSE: Towards unified multimodal sentiment analysis and emotion recognition
Guimin Hu, Ting-En Lin, Yi Zhao, Guangming Lu, Yuchuan Wu, and Yongbin Li. 2022 · 2022
Later among the works it cites.
Modular and parameter-efficient multimodal fusion with prompting
Sheng Liang, Mengjie Zhao, and Hinrich Schuetze. 2022 · 2022
Later among the works it cites.
P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2022 · 2022
Later among the works it cites.
Maple: Multi-modal prompt learning
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan. 2023 · 2023
Later among the works it cites.
Multimodal prompting with missing modalities for visual recognition
Yi-Lun Lee, Yi-Hsuan Tsai, Wei-Chen Chiu, and Chen-Yu Lee. 2023 · 2023
Later among the works it cites.