Summary of A Transformer-based Model For the Prediction Of Human Gaze Behavior on Videos, by Suleyman Ozdel et al.
A Transformer-Based Model for the Prediction of Human Gaze Behavior on Videos
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 proposed paper introduces a novel approach for simulating human gaze behavior in video understanding tasks, utilizing a transformer-based reinforcement learning algorithm. This method trains an agent to act as a human observer, watching videos and replicating human gaze patterns. The eye-tracking dataset used is gathered from videos generated by the VirtualHome simulator, focusing on activity recognition. Experimental results demonstrate the effectiveness of the gaze prediction method in replicating human gaze behavior and its applicability for downstream tasks using real human-gaze input. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make computers understand what we’re looking at when watching videos. This is important because it helps machines learn how people watch videos, which can be useful in many areas like video analysis or activity recognition. The researchers developed an algorithm that uses machine learning to train a computer agent to act like a person watching a video and tracking their gaze. They tested this approach using a large dataset of videos generated from the VirtualHome simulator. The results show that their method is very good at predicting human gaze behavior, which can be used in many applications. |
Keywords
» Artificial intelligence » Activity recognition » Machine learning » Reinforcement learning » Tracking » Transformer
