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English(EN) GM-GReFEL: Geometry-Guided Spatiotemporal State-Space Learning for Robust Dynamic Facial Expression Recognition

新的GM-GReFEL模型增强面部表情识别能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的时空状态空间网络GM-GReFEL，用于动态面部表情识别。该模型通过采用与序列长度呈线性关系的Modulated GroupMamba骨干网络，解决了诸如剪辑长度的二次计算成本和噪声标签等挑战。一个几何感知模块通过在网络不确定时整合面部几何信息，进一步增强了鲁棒性。GM-GReFEL在DFEW、MAFW和FERV39k等基准数据集上表现出有竞争力，并且在时间建模方面观察到了适度的益处。 AI

影响 为分析视频中的动态面部表情引入了一种更有效、更鲁棒的方法。

排序理由 这是一篇详细介绍面部表情识别新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的GM-GReFEL模型增强面部表情识别能力

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这是一篇详细介绍面部表情识别新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yudhistira Arditya Pratama, Yi-Zeng Hsieh ·

    GM-GReFEL: Geometry-Guided Spatiotemporal State-Space Learning for Robust Dynamic Facial Expression Recognition

    arXiv:2610.31140v1 Announce Type: cross Abstract: Recognizing facial expressions in unconstrained video suffers from two coupled problems: self-attention grows quadratically in cost with clip length, and crowdsourced labels are noisy. We present GM-GReFEL, a spatiotemporal state-…

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