Summary of Recent Advances Of Multimodal Continual Learning: a Comprehensive Survey, by Dianzhi Yu et al.
Recent Advances of Multimodal Continual Learning: A Comprehensive Survey
by Dianzhi Yu, Xinni Zhang, Yankai Chen, Aiwei Liu, Yifei Zhang, Philip S. Yu, Irwin King
First submitted to arxiv on: 7 Oct 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 paper presents a comprehensive survey on multimodal continual learning (MMCL), which aims to empower machine learning models to learn from new data while building upon previously acquired knowledge without forgetting. The authors categorize existing MMCL methods into four categories: regularization-based, architecture-based, replay-based, and prompt-based methods, explaining their methodologies and highlighting key innovations. Additionally, the paper summarizes open MMCL datasets and benchmarks, and discusses promising future directions for investigation and development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal continual learning is a way to teach machines to learn from new data while keeping what they already know. This is important because machines can forget old information as they learn new things. The researchers did a big study on how people are trying to solve this problem, and they found four main ways: regularization-based methods, architecture-based methods, replay-based methods, and prompt-based methods. They also listed some open datasets and benchmarks that scientists can use to test their ideas. |
Keywords
» Artificial intelligence » Continual learning » Machine learning » Prompt » Regularization
