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arXiv Machine Learning By Yudhistira Arditya Pratama, Yi-Zeng Hsieh

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

Read the original on arXiv Machine Learning →

GM-GReFEL is a geometry‑guided spatiotemporal state‑space network for dynamic facial expression recognition that replaces a Vision Transformer with a Modulated GroupMamba backbone, reducing self‑attention cost to linear time. It incorporates a reliability module that mixes classifier outputs with geometric anchor votes based on prediction entropy, and is trained via a Triple Loss after static‑to‑dynamic weight transfer. In five‑fold cross‑validation it achieves competitive UAR/WAR scores on DFEW, MAFW, and FERV39k, with modest temporal modeling benefits and latency comparable to ViT‑B/16.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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