Attention-optimized vision-enhanced prompt learning for few-shot multi-modal sentiment analysis
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00521-024-10297-w.pdf
Reference28 articles.
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2. Cai Y, Cai H, Wan X (2019) Multi-modal sarcasm detection in twitter with hierarchical fusion model. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2506–2515. Association for Computational Linguistics, Florence, Italy . https://doi.org/10.18653/v1/P19-1239
3. Jia J, Zhou S, Yin Y, Wu B, Chen W, Meng F, Wang Y (2019) Inferring emotions from large-scale internet voice data. IEEE Trans Multimed 21(7):1853–1866. https://doi.org/10.1109/TMM.2018.2887016
4. Niu T, Zhu S, Pang L, El Saddik A (2016) Sentiment analysis on multi-view social data. In: Tian Q, Sebe N, Qi G-J, Huet B, Hong R, Liu X (eds) MultiMedia Modeling Lecture Notes in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_2
5. Yu Y, Zhang D, Li S (2022) Unified multi-modal pre-training for few-shot sentiment analysis with prompt-based learning. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 189–198. ACM, Lisboa Portugal https://doi.org/10.1145/3503161.3548306
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