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New GM-GReFEL Model Enhances Facial Expression Recognition

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced GM-GReFEL, a novel spatiotemporal state-space network designed for dynamic facial expression recognition. This model addresses challenges like quadratic computational costs with clip length and noisy labels by employing a Modulated GroupMamba backbone that scales linearly with sequence length. A geometry-aware module further enhances robustness by integrating facial geometry information when the network is uncertain. GM-GReFEL demonstrates competitive performance on benchmark datasets such as DFEW, MAFW, and FERV39k, with a modest benefit observed for temporal modeling. AI

IMPACT Introduces a more efficient and robust method for analyzing dynamic facial expressions in videos.

RANK_REASON This is a research paper detailing a new model for facial expression recognition. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New GM-GReFEL Model Enhances Facial Expression Recognition

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This is a research paper detailing a new model for facial expression recognition. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [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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