← Back to all news
arXiv Machine Learning

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

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.

Related stories

arXiv AI
2d ago

PairCLIP-SWA: Similarity-Guided Balancing and Stochastic Weight Averaging for Generalized Deepfake Image Detection with OpenCLIP

PairCLIP‑SWA is a single‑backbone deepfake detector that fine‑tunes an OpenCLIP ViT‑L/14 visual tower using a class‑balanced corpus created by CLIP‑similarity‑guided subsampling and then applies stochastic weight averaging over the fine‑tuning trajectory. Trained on the IEEE SP Cup 2025 (DF‑Wild‑Cup) benchmark, the weight‑averaged model achieves 97.04 % accuracy, 98.64 % AU‑ROC and a 4.07 % equal error rate—outperforming the best published three‑backbone ensemble. The study shows that averaging stabilizes validation performance, prevents misranking on disjoint test sets, and that CLIP pre‑training, rather than ensembling, is the key driver of accuracy.

By Md. Nakib, Sudipto Sarkar, A. J. A. Jubayer Talukder, Mohammad Ariful Haque
fine-tuningbenchmarks
More like this →
arXiv Computer Vision
Sep 25

Can Frozen Hyperspherical Features Guide the Selection of Pseudo Masks?

The paper introduces SphereTrust, a method that uses frozen self‑supervised hyperspherical features to evaluate and rank candidate masks produced by foundation segmenters like SAM. By measuring angular contrast, foreground coverage, and image‑frame contact, SphereTrust can select high‑quality masks in 0.55 s per image and outperforms existing baselines on multiple segmentation tasks. The selected masks are then used as priors to train student models, improving performance on several benchmark datasets.

By Xinge Guo, Fengyang Xiao, Dingming Zhang, Yuhan Chen, Rihan Zhang, Xingjian Li, Tianyang Wang, Chunming He, Sina Farsiu
computer-vision
More like this →