GM-GReFEL：几何引导的状态空间网络用于动态表情识别
GM-GReFEL: Geometry-Guided Spatiotemporal State-Space Learning for Robust Dynamic Facial Expression Recognition
一篇论文提出 GM-GReFEL，用状态空间网络替代 ViT 做视频表情识别，算力线性增长，还用几何锚点纠错，MAFW 上跑赢所有对比方法。
GM-GReFEL 用 Modulated GroupMamba 骨干替换时空 Vision Transformer，六个通道组分别扫描，计算复杂度随序列长度线性增长。其 GReFEL 可靠性模块将预测熵转化为分类器输出与可训练几何锚点投票之间的混合权重。在 DFEW 五折交叉验证中最佳折达到 66.57%/76.46% UAR/WAR，在 MAFW 上以 45.66%/62.27% 超过所有对比方法。五折均值在 DFEW 上为 64.07%/74.54%，与近期大规模预训练模型相当，单片段延迟 47.4 毫秒，接近 ViT-B/16 的 46.8 毫秒。
GM-GReFEL: Geometry-Guided Spatiotemporal State-Space Learning for Robust Dynamic Facial Expression Recognition
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-space network with a geometry-aware decision stage. Its Modulated GroupMamba backbone replaces a spatiotemporal Vision Transformer. Six channel groups are scanned separately, four over the frame plane and two over time, so the cost grows linearly with sequence length ($\mathcal{O}(N)$); after each layer, a Channel Affinity Modulation gate mixes the groups again. On top of the backbone, a GReFEL reliability module turns the normalized entropy of each prediction into a mixing weight between the classifier output and a vote from trainable geometric anchors, so that facial geometry counts for more when the network is unsure. The model is initialized by static-to-dynamic weight transfer and trained with a Triple Loss. In five-fold cross-validation, the best fold scores 66.57\%/76.46\% UAR/WAR on DFEW, 0.3 points or less below the strongest compared method, and 45.66\%/62.27\% on MAFW, ahead of all compared methods; on FERV39k the mean over five splits, 42.30\%/52.66\%, is competitive. The five-fold means are 64.07\%/74.54\% on DFEW, on par with recent large pre-trained models, and 38.90\%/54.37\% on MAFW, below the best of them. A paired $t$-test on DFEW indicates a modest benefit of temporal modeling (+1.61 UAR points, one-sided $p=0.031$). Attention stays on plausible facial regions, and latency is close to a ViT-B/16 baseline (47.4 vs.\ 46.8 ms per clip). Code: https://github.com/ardiytama/GroupMamba-DFER.
