无需人工标注，用先验知识自动分割眼球区域。
Towards Unsupervised Eye-Region Segmentation for Eye Tracking
- 利用人眼先验信息生成粗略定位信号，指导无监督分割。
- 在无标注数据下，瞳孔和虹膜分割达90%、全眼区域达85%的监督学习性能。
- 适合缺乏标注数据的头戴式VR/AR设备眼动追踪场景。
图像眼动追踪需先定位眼球并分割出各部分（如瞳孔、虹膜），是当前头戴式虚拟/增强现实设备的核心模块。传统训练依赖繁琐的人工标注。本文探索无监督方法：首先利用人眼先验信息从图像中提取粗略线索；在此稀疏且噪声较大的线索基础上，训练分割网络逐步精确定位各部分。为提升精度，先通过预训练的Segment Anything Model（SAM）自动优化眼区指示，再采用端到端、渐进式且基于先验的训练策略。实验表明，该无监督方法在无标注数据下，瞳孔与虹膜分割性能达到监督学习的90%，整个眼区分割达到85%。
原文摘要 · Abstract (English)
Finding the eye and parsing out the parts (e.g. pupil and iris) is a key prerequisite for image-based eye tracking, which has become an indispensable module in today's head-mounted VR/AR devices. However, a typical route for training a segmenter requires tedious handlabeling. In this work, we explore an unsupervised way. First, we utilize priors of human eye and extract signals from the image to establish rough clues indicating the eye-region structure. Upon these sparse and noisy clues, a segmentation network is trained to gradually identify the precise area for each part. To achieve accurate parsing of the eye-region, we first leverage the pretrained foundation model Segment Anything (SAM) in an automatic way to refine the eye indications. Then, the learning process is designed in an end-to-end manner following progressive and prior-aware principle. Experiments show that our unsupervised approach can easily achieve 90% (the pupil and iris) and 85% (the whole eye-region) of the performances under supervised learning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。