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Found 3 papers, 2 papers with code
Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)
The advent of foundation models signals a new era in artificial intelligence.
LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images
This problem is exacerbated by both hardware-induced variations in eye images and inherent biological differences across the recorded participants, leading to both feature and pixel-level variance that hinders the generalizability of models trained on specific datasets.
Precise localization of corneal reflections in eye images using deep learning trained on synthetic data
Our method outperformed state-of-the-art algorithmic methods on real eye images with a 35% reduction in terms of spatial precision, and performed on par with state-of-the-art on simulated images in terms of spatial accuracy. We conclude that our method provides a precise method for CR center localization and provides a solution to the data availability problem which is one of the important common roadblocks in the development of deep learning models for gaze estimation.
