Mixture of Experts

Also known as MoE

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Latest papers 217

37 papers in the last four weeks, up 32% on the four weeks before. 0.2% of all new papers.

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SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation
Feb 22, 2026Yujie Lu, Jingwen Li, Sibo Ju +5Medical Image Segmentation Vision Foundation Model Adaptation
Sichuan University · Xinjiang University · Fuzhou University +1
DeepSVU: Towards In-depth Security-oriented Video Understanding via Unified Physical-world Regularized MoE
Feb 20, 2026Yujie Jin, Wenxin Zhang, Jingjing Wang +1Video Understanding Mixture of Experts
School of Computer Science and Technology, Soochow University, Suzhou, China
A Theoretical Framework for Modular Learning of Robust Generative Models
Feb 19, 2026Corinna Cortes, Mehryar Mohri, Yutao ZhongGenerative Modeling Modular Neural Networks
Google Research, New York, NY · Courant Institute of Mathematical Sciences, New York, NY
ClusIR: Towards Cluster-Guided All-in-One Image Restoration
Dec 11, 2025Shengkai Hu, Jiaqi Ma, Xu Zhang +3Mixture of Experts All-in-One Image Restoration
Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, United Arab Emirates · Nanyang Technological University Singapore, Singapore
UMM-RM: An Upcycle-and-Merge MoE Reward Model for Mitigating Reward Hacking
Nov 30, 2025Lingling Fu, Yongfu XueReward Modeling Reward Hacking
Guangxi University
MEGO: Learning Mixture-of-Experts for General-Purpose Binary Optimization
May 29, 2024Shengcai Liu, Zhiyuan Wang, Yew-Soon Ong +2Black-Box Optimization Mixture of Experts
Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China · College of Computing & Data Science, Nanyang Technological University, Singapore 639798 · School of Data Science, Lingnan University, Hong Kong, China
Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts
Jul 14, 2019Thin Nguyen-Van, Faicel Chamroukhi, Ha Hoang Van +1Mixture of Experts Sparse Mixture-of-Experts
Faculty of Mathematics and Computer Science, University of Science, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam · IRT SystemX, 91120 Palaiseau, France +1

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SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation

Yujie Lu, Jingwen Li, Sibo Ju, Yanzhou Su+4

Sichuan University · Xinjiang University · Fuzhou University+1

Medical Image Segmentation Vision Foundation Model Adaptation Promptable Image Segmentation Mixture of Experts

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Medical image segmentation is vital for clinical diagnosis and quantitative analysis, yet remains challenging due to the heterogeneity of imaging modalities and the high cost of pixel-level annotations. Although general interactive segmentation models like SAM have achieved remarkable progress, their transfer to medical imaging still faces two key bottlenecks: (i) the lack of adaptive mechanisms for modality- and anatomy-specific tasks, which limits generalization in out-of-distribution medical scenarios; and (ii) current medical adaptation methods fine-tune on large, heterogeneous datasets without selection, leading to noisy supervision, higher cost, and negative transfer. To address these issues, we propose SegMoTE, an efficient and adaptive framework for medical image segmentation. SegMoTE preserves SAM's original prompt interface, efficient inference, and zero-shot generalization while introducing only a small number of learnable parameters to dynamically adapt across modalities and tasks. In addition, we design a progressive prompt tokenization mechanism that enables fully automatic segmentation, significantly reducing annotation dependence. Trained on MedSeg-HQ, a curated dataset less than 1% of existing large-scale datasets, SegMoTE achieves SOTA performance across diverse imaging modalities and anatomical tasks. It represents the first efficient, robust, and scalable adaptation of general segmentation models to the medical domain under extremely low annotation cost, advancing the practical deployment of foundation vision models in clinical applications.