Summary of Semantic-guided Multimodal Sentiment Decoding with Adversarial Temporal-invariant Learning, by Guoyang Xu et al.
Semantic-Guided Multimodal Sentiment Decoding with Adversarial Temporal-Invariant Learning
by Guoyang Xu, Junqi Xue, Yuxin Liu, Zirui Wang, Min Zhang, Zhenxi Song, Zhiguo Zhang
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to multimodal sentiment analysis, addressing the issue of incomplete modality representations with noise. The authors introduce temporal-invariant learning to capture long-term temporal dynamics and enhance representation quality. A semantic-guided fusion module is also proposed to facilitate cross-modal interactions gated by modality-invariant representations. Additionally, a modality discriminator is introduced to disentangle modality-invariant and modality-specific subspaces. Experimental results on two public datasets demonstrate the superiority of the model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make computers better at understanding people’s emotions from different types of data like text, images, or videos. Right now, these computer models often get confused because they don’t account for the fact that some patterns in the data repeat over time. To fix this problem, the authors create a new way of learning that helps the model understand these repeating patterns and make better predictions. They also add a special tool to help the model combine different types of data together correctly. This helps the model learn more about people’s emotions and make more accurate predictions. |
