Summary of Human Action Anticipation: a Survey, by Bolin Lai et al.
Human Action Anticipation: A Survey
by Bolin Lai, Sam Toyer, Tushar Nagarajan, Rohit Girdhar, Shengxin Zha, James M. Rehg, Kris Kitani, Kristen Grauman, Ruta Desai, Miao Liu
First submitted to arxiv on: 17 Oct 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 The paper surveys the growing field of predicting human behavior in computer vision, driven by applications like autonomous vehicles and digital assistants. It covers recent innovations and new large-scale datasets for model training and evaluation, summarizing widely-used metrics for different tasks and providing a performance comparison of existing approaches on eleven action anticipation datasets. The survey aims to tie together the fragmented literature on behavior prediction, including tasks like action anticipation, activity forecasting, intent prediction, and goal prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can predict what people will do next. It’s useful for things like self-driving cars and virtual assistants. Researchers have been working on this problem and have come up with different ways to solve it. The paper shows which methods are most effective and provides a benchmark to compare them. It also talks about the data needed to train these models. |
