Prompt Learning for Multimodal Intent Recognition with Modal Alignment Perception-Reference-Cited by-同舟云学术

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Prompt Learning for Multimodal Intent Recognition with Modal Alignment Perception

Published:2024-08-10 Issue: Volume: Page:
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Chen Yuzhao,Zhu Wenhua,Yu Weilun,Xue Hongfei,Fu Hao,Lin Jiali,Jiang Dazhi

Funder

Natural Science Foundation of Guangdong Province

National Natural Science Foundation of China

Science and Technology Major Project of Guangdong Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12559-024-10328-7.pdf

Reference51 articles.

1. Yu W, Li C, Hu X, Zhu W, Cambria E, Jiang D. Dialogue emotion model based on local–global context encoder and commonsense knowledge fusion attention. Int J Mach Learn Cybern. 2024. pp. 1–15.

2. Jiang D, Wei R, Liu H, Wen J, Tu G, Zheng L, Cambria EA. A multitask learning framework for multimodal sentiment analysis. In: 2021 International conference on data mining workshops (ICDMW). IEEE; 2021. pp. 151–7.

3. Zhang H, Li X, Xu H, Zhang P, Zhao K, Gao K. TEXTOIR: an integrated and visualized platform for text open intent recognition. arXiv:2110.15063. 2021.

4. Zhang H, Xu H, Lin T-E. Deep open intent classification with adaptive decision boundary. In: Proceedings of the AAAI conference on artificial intelligence, vol. 35. 2021. pp. 14374–82.

5. Zhang H, Xu H, Lin T-E, Lyu R. Discovering new intents with deep aligned clustering. In: Proceedings of the AAAI conference on artificial intelligence, vol. 35. 2021. pp. 14365–73.

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