Semantic-specific multimodal relation learning for 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/s00521-024-09644-8.pdf
Reference35 articles.
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3. Sun Z, Sarma P, Sethares W, Liang Y (2020) Learning relationships between text, audio, and video via deep canonical correlation for multimodal language analysis. In: Conference on artificial intelligence, vol 34, pp 8992–8999
4. Hazarika D, Zimmermann R, Poria S (2020) MISA: modality-invariant and -specific representations for multimodal sentiment analysis. In: International conference on multimedia (MM), pp 1122–1131. https://doi.org/10.1145/3394171.3413678
5. Zhu H, Zheng Z, Soleymani M, Nevatia R (2022) Self-supervised learning for sentiment analysis via image-text matching. In: IEEE international conference on acoustics, speech and signal processing, pp 1710–1714
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