Summary of Meta-learn Unimodal Signals with Weak Supervision For Multimodal Sentiment Analysis, by Sijie Mai et al.
Meta-Learn Unimodal Signals with Weak Supervision for Multimodal Sentiment Analysis
by Sijie Mai, Yu Zhao, Ying Zeng, Jianhua Yao, Haifeng Hu
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 meta uni-label generation (MUG) framework aims to address the noisy label problem in multimodal sentiment analysis by leveraging weak supervision from annotated multimodal labels. The framework consists of a contrastive-based projection module, which bridges the gap between unimodal and multimodal representations, and a meta uni-label correction network (MUCN). MUG uses a bi-level optimization strategy to train MUCN with explicit supervision via denoising tasks for both unimodal and multimodal learning. The framework is evaluated on multimodal sentiment analysis benchmarks, outperforming competitive baselines and demonstrating the ability to learn accurate unimodal labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The MUG framework helps improve sentiment analysis by learning unimodal labels even when there are no annotations available. This is important because most previous studies have relied on multimodal labels for training, which can be noisy or imprecise. The new approach uses a special type of neural network to correct these issues and learn better unimodal labels. The results show that this method works well and can be used in real-world applications. |
Keywords
» Artificial intelligence » Neural network » Optimization
