Summary of Delving Into Multi-modal Multi-task Foundation Models For Road Scene Understanding: From Learning Paradigm Perspectives, by Sheng Luo et al.
Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives
by Sheng Luo, Wei Chen, Wanxin Tian, Rui Liu, Luanxuan Hou, Xiubao Zhang, Haifeng Shen, Ruiqi Wu, Shuyi Geng, Yi Zhou, Ling Shao, Yi Yang, Bojun Gao, Qun Li, Guobin Wu
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 presents a comprehensive survey on multi-modal and multi-task visual understanding foundation models (MM-VUFMs) specifically designed for road scenes. The authors highlight the capabilities of MM-VUFMs in processing diverse modalities and handling various driving-related tasks with powerful adaptability, contributing to a more holistic understanding of the surrounding scene. They provide an overview of common practices, including task-specific models, unified multi-modal models, unified multi-task models, and foundation model prompting techniques, as well as advanced capabilities in open-world understanding, efficient transfer for road scenes, continual learning, interactive, and generative capability. The authors also discuss key challenges and future trends, such as closed-loop driving systems, interpretability, embodied driving agents, and world models. To facilitate researchers, the authors have established a continuously updated repository at this URL, providing access to the latest developments in MM-VUFMs for road scenes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper looks at how foundation models can help make cars smarter by letting them understand what’s happening around them better. Foundation models are special kinds of artificial intelligence that can learn from many different sources and handle lots of tasks at once. The authors explore how these models can be used in self-driving cars to make decisions about what to do next, and they also discuss the challenges and future directions for this technology. |
Keywords
* Artificial intelligence * Continual learning * Multi modal * Multi task * Prompting
