Summary of About Time: Advances, Challenges, and Outlooks Of Action Understanding, by Alexandros Stergiou and Ronald Poppe
About Time: Advances, Challenges, and Outlooks of Action Understanding
by Alexandros Stergiou, Ronald Poppe
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 of recent advancements in video action understanding, highlighting impressive performance leaps across various tasks. The review focuses on the challenges, datasets, and seminal works that have driven progress in uni- and multi-modal action understanding. Key contributions include coarse- and fine-grained descriptions of scenes, segment extraction for queries, video synthesis, and context prediction. The survey is organized around three temporal scopes: recognition, prediction, and forecasting, which allows for the identification of specific action modeling and video representation challenges. Finally, the paper outlines future directions to address current shortcomings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video action understanding has made huge progress, with machines getting better at describing what’s happening in videos, finding parts that match queries, filling in gaps in videos, and predicting what will happen next. This survey looks back at how we got here and what challenges we still need to solve. It covers big datasets, important papers, and three main types of tasks: recognizing actions that are fully shown, predicting ongoing actions that are partially shown, and forecasting future actions that haven’t happened yet. |
Keywords
* Artificial intelligence * Multi modal
