Summary of Elf-ua: Efficient Label-free User Adaptation in Gaze Estimation, by Yong Wu et al.
ELF-UA: Efficient Label-Free User Adaptation in Gaze Estimation
by Yong Wu, Yang Wang, Sanqing Qu, Zhijun Li, Guang Chen
First submitted to arxiv on: 13 Jun 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 tackles the challenge of user-adaptive 3D gaze estimation, which is crucial for applications like virtual reality and human-computer interaction. The problem lies in the fact that person-independent gaze estimation models are limited due to interpersonal anatomical differences. To address this issue, the authors propose a new problem called efficient label-free user adaptation in gaze estimation. Their approach uses meta-learning to adapt to a new user with only a few unlabeled images. A key innovation is the use of a generalization bound from domain adaptation to define the loss function in meta-learning. The method combines both labeled source data and unlabeled person-specific data during training, enabling effective adaptation without requiring labeled images of the target person. The proposed approach is validated on several challenging benchmarks, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to control a computer or virtual reality experience just by looking at it. That’s what this paper is all about: making gaze estimation (tracking where someone looks) more accurate and personalized for each individual user. Right now, it’s hard to make these systems work well because everyone’s eyes are slightly different. The authors of this paper came up with a new way to make these systems adapt to each person without needing lots of labeled images or information about the person. They tested their approach on several challenges and found that it worked really well. |
Keywords
» Artificial intelligence » Domain adaptation » Generalization » Loss function » Meta learning » Tracking
