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New research tackles gaze estimation with trajectory awareness and mobile optimization

By PulseAugur Editorial · [2 sources] ·

Two new research papers address gaze estimation, a technology crucial for applications like driver monitoring and human-computer interaction. The first paper introduces EyeTAG, a framework that incorporates gaze trajectory as an explicit variable, improving accuracy by reducing jitter and saccade bias. The second paper presents UniGaze-H, a lightweight model designed for real-time gaze tracking on mobile devices, which enhances generalization in unconstrained scenarios by using data augmentation and multi-task learning. AI

IMPACT Advances in gaze estimation could improve human-computer interaction and driver monitoring systems.

RANK_REASON Two academic papers published on arXiv detailing new methods for gaze estimation.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research tackles gaze estimation with trajectory awareness and mobile optimization

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Jungmin Lee, Niamat Ullah, Yoseob Han ·

    EyeTAG: Eye Trajectory-Aware Gaze Estimation

    arXiv:2610.00922v1 Announce Type: new Abstract: Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction. Single-frame methods predict each frame independently, so consecutive outputs fluctuate as jitter. Multi-fram…

  2. arXiv cs.CV TIER_1 English(EN) · Zhenhao Li, Zheng Liu, Seunghyun Lee, Amin Fadaeinejad, Yuanhao Yu ·

    Real-time Appearance-based Gaze Estimation for Open Domains

    arXiv:2603.26945v2 Announce Type: replace Abstract: Appearance-based gaze estimation (AGE) has achieved remarkable performance in constrained settings, yet we reveal a significant generalization gap where existing AGE models often fail in practical, unconstrained scenarios, parti…

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