Summary of Sentiment-oriented Transformer-based Variational Autoencoder Network For Live Video Commenting, by Fengyi Fu et al.
Sentiment-oriented Transformer-based Variational Autoencoder Network for Live Video Commenting
by Fengyi Fu, Shancheng Fang, Weidong Chen, Zhendong Mao
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed Sentiment-oriented Transformer-based Variational Autoencoder (So-TVAE) network aims to generate diverse and sentiment-rich video comments. By combining a sentiment-oriented diversity encoder module with a batch attention module, the So-TVAE model achieves semantic diversity under sentiment guidance, outperforming state-of-the-art methods on Livebot and VideoIC datasets in terms of comment quality and diversity. The model’s ability to balance sentimental samples alleviates the issue of missing data caused by popularity imbalance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The So-TVAE network generates live video comments with multiple sentiments and semantics. It uses a sentiment-oriented diversity encoder module, which combines Variational Autoencoder (VAE) and random mask mechanisms, to achieve semantic diversity under sentiment guidance. The model also incorporates a batch attention module to alleviate data imbalance issues caused by popularity variation. The So-TVAE outperforms state-of-the-art methods in generating high-quality and diverse video comments. |
Keywords
» Artificial intelligence » Attention » Encoder » Mask » Semantics » Transformer » Variational autoencoder
