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English(EN) GMGaze: MoE-Based Context-Aware Gaze Estimation with CLIP and Multiscale Transformer

GMGaze模型利用CLIP和多尺度Transformer实现SOTA注视点估计

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新颖的注视点估计方法GMGaze，该方法利用多尺度Transformer架构并结合上下文感知条件。该方法通过早期融合图像特征和采用混合专家（MoE）设计以实现高效计算扩展，解决了现有模型的局限性。GMGaze在多个基准测试中展现了最先进的性能，在域内和跨域注视点估计任务中均提高了准确性。 AI

影响 引入了一种新的注视点估计架构，有望提高需要眼动追踪的应用的准确性和效率。

排序理由 介绍新模型架构和基准测试结果的学术论文。

在 arXiv cs.CV 阅读 →

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

GMGaze模型利用CLIP和多尺度Transformer实现SOTA注视点估计

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Xinyuan Zhao, Yihang Wu, Ahmad Chaddad, Sarah A. Alkhodair, Reem Kateb ·

    GMGaze：基于MoE的上下文感知眼动追踪，结合CLIP和多尺度Transformer

    arXiv:2605.00799v1 Announce Type: new Abstract: Gaze estimation methods commonly use facial appearances to predict the direction of a person gaze. However, previous studies show three major challenges with convolutional neural network (CNN)-based, transformer-based, and contrasti…

  2. arXiv cs.CV TIER_1 English(EN) · Reem Kateb ·

    GMGaze：基于MoE的上下文感知眼动追踪，结合CLIP和多尺度Transformer

    Gaze estimation methods commonly use facial appearances to predict the direction of a person gaze. However, previous studies show three major challenges with convolutional neural network (CNN)-based, transformer-based, and contrastive language-image pre-training (CLIP)-based meth…

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