Summary of Suppressing Uncertainty in Gaze Estimation, by Shijing Wang et al.
Suppressing Uncertainty in Gaze Estimation
by Shijing Wang, Yaping Huang
First submitted to arxiv on: 17 Dec 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 proposes a novel approach to address uncertainties in gaze estimation, which are caused by low-quality images or incorrect annotations. The authors introduce a triplet-label consistency measurement, called Suppressing Uncertainty in Gaze Estimation (SUGE), that estimates and reduces these uncertainties. This is achieved through the calculation of a “neighboring label” based on linearly weighted projections from neighboring samples, which can be used to measure the quality of both images and labels. The approach incorporates sample weighting and label correction strategies to reduce the negative effects of unqualified images and wrong labels. Experimental results demonstrate that SUGE achieves state-of-the-art performance on gaze estimation benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in guessing where people are looking. When trying to guess, we might get bad pictures or make mistakes when labeling what’s in those pictures. These mistakes can make it harder for computers to learn how to do this task well. The authors come up with a way to measure and reduce these mistakes by comparing the picture with its neighbors. This helps computers decide which pictures are good and which aren’t, so they can focus on learning from the best ones. |
