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English(EN) MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data

MambaGaze 框架使用 Mamba-2 进行认知负荷评估

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了 MambaGaze，一个利用眼动追踪数据准确评估认知负荷的新框架。该系统利用双向 Mamba-2 有效建模长程时间依赖性，并采用 XMD 编码方法显式处理因眨眼等原因造成的缺失数据。MambaGaze 在基准数据集上的表现优于现有模型，并可在 NVIDIA Jetson 平台等边缘设备上进行实时部署。 AI

影响 引入了一种新颖的实时认知负荷评估方法，有可能在安全关键系统中实现更具响应性的人机交互。

排序理由 该集群包含一篇详细介绍新模型及其实验结果的学术论文。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Amir Mousavi, Mohammad Sadegh Sirjani, Erfan Nourbakhsh, Mimi Xie, Rocky Slavin, Leslie Neely, John Davis, John Quarles ·

    MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data

    arXiv:2605.22775v1 Announce Type: new Abstract: Real-time cognitive load assessment from eye-tracking signals could potentially enable adaptive human-centered-AI such as safety-critical applications such as driver vigilance monitoring or automated flight deck assistance, yet two …

  2. arXiv cs.AI TIER_1 English(EN) · John Quarles ·

    MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data

    Real-time cognitive load assessment from eye-tracking signals could potentially enable adaptive human-centered-AI such as safety-critical applications such as driver vigilance monitoring or automated flight deck assistance, yet two challenges persist: handling frequent data missi…

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