Summary of Multimodal Fusion on Low-quality Data: a Comprehensive Survey, by Qingyang Zhang et al.
Multimodal Fusion on Low-quality Data: A Comprehensive Survey
by Qingyang Zhang, Yake Wei, Zongbo Han, Huazhu Fu, Xi Peng, Cheng Deng, Qinghua Hu, Cai Xu, Jie Wen, Di Hu, Changqing Zhang
First submitted to arxiv on: 27 Apr 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 This paper explores multimodal fusion, which combines information from multiple sources to improve prediction accuracy. Despite significant progress in autonomous driving and medical diagnosis, the reliability of multimodal fusion under low-quality data remains unexplored. The authors identify four main challenges: noisy data, incomplete data, imbalanced data, and quality-varying data. They present a comprehensive taxonomy of these challenges, enabling researchers to understand the state of the field and identify potential directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal fusion helps machines make better predictions by combining information from different sources. This is important for tasks like self-driving cars and medical diagnosis. However, it’s not clear how well this works when the data is low-quality. The authors look at the problems that happen when doing multimodal fusion with bad data, and they come up with a way to categorize these issues. |
