Summary of Gaze-guided Graph Neural Network For Action Anticipation Conditioned on Intention, by Suleyman Ozdel et al.
Gaze-Guided Graph Neural Network for Action Anticipation Conditioned on Intention
by Suleyman Ozdel, Yao Rong, Berat Mert Albaba, Yen-Ling Kuo, Xi Wang, Enkelejda Kasneci
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Human-Computer Interaction (cs.HC); 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 introduces the Gaze-guided Action Anticipation algorithm to predict actions in videos based on partial video input. The approach utilizes a Graph Neural Network to recognize an agent’s intention and predict subsequent action sequences. A dataset containing household activities generated in VirtualHome, along with human gaze data, is used for evaluation. Results show a 7% improvement in accuracy for 18-class intention recognition compared to state-of-the-art techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper teaches us how computers can better understand videos by looking at where humans are focusing their attention. By using this information, the computer can make more accurate predictions about what’s going to happen next in a video. The researchers created an algorithm called Gaze-guided Action Anticipation that does just that. They tested it with a special dataset of household activities and found that it worked really well, beating other methods by 7%. This is important because it helps us better understand videos and could be used in many different applications. |
Keywords
* Artificial intelligence * Attention * Graph neural network
