MoTE: Mixture of Ternary Experts for Memory-efficient Large Multimodal Models
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Hongyu, Xu, Jiayu, Wang, Ruiping, Feng, Yan, Zhai, Yitao, Pei, Peng, Cai, Xunliang, Chen, Xilin |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge Transfer
by: Zhu, Minghao, et al.
Published: (2024)
by: Zhu, Minghao, et al.
Published: (2024)
Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment
by: Liu, Zhili, et al.
Published: (2024)
by: Liu, Zhili, et al.
Published: (2024)
Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models
by: Romero, Miguel, et al.
Published: (2025)
by: Romero, Miguel, et al.
Published: (2025)
MoTE: Mixture of Task-specific Experts for Pre-Trained ModelBased Class-incremental Learning
by: Li, Linjie, et al.
Published: (2025)
by: Li, Linjie, et al.
Published: (2025)
M4U: Evaluating Multilingual Understanding and Reasoning for Large Multimodal Models
by: Wang, Hongyu, et al.
Published: (2024)
by: Wang, Hongyu, et al.
Published: (2024)
Glance and Focus: Memory Prompting for Multi-Event Video Question Answering
by: Bai, Ziyi, et al.
Published: (2024)
by: Bai, Ziyi, et al.
Published: (2024)
BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation
by: Wang, Hongyu, et al.
Published: (2025)
by: Wang, Hongyu, et al.
Published: (2025)
SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation
by: Lu, Yujie, et al.
Published: (2026)
by: Lu, Yujie, et al.
Published: (2026)
Blocks as Probes: Dissecting Categorization Ability of Large Multimodal Models
by: Fu, Bin, et al.
Published: (2024)
by: Fu, Bin, et al.
Published: (2024)
Robotic Programmer: Video Instructed Policy Code Generation for Robotic Manipulation
by: Xie, Senwei, et al.
Published: (2025)
by: Xie, Senwei, et al.
Published: (2025)
MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models
by: Shen, Leyang, et al.
Published: (2024)
by: Shen, Leyang, et al.
Published: (2024)
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts
by: Li, Jiachen, et al.
Published: (2024)
by: Li, Jiachen, et al.
Published: (2024)
MoDES: Accelerating Mixture-of-Experts Multimodal Large Language Models via Dynamic Expert Skipping
by: Huang, Yushi, et al.
Published: (2025)
by: Huang, Yushi, et al.
Published: (2025)
RoboPCA: Pose-centered Affordance Learning from Human Demonstrations for Robot Manipulation
by: Xiao, Zhanqi, et al.
Published: (2026)
by: Xiao, Zhanqi, et al.
Published: (2026)
MoVA: Adapting Mixture of Vision Experts to Multimodal Context
by: Zong, Zhuofan, et al.
Published: (2024)
by: Zong, Zhuofan, et al.
Published: (2024)
MoIIE: Mixture of Intra- and Inter-Modality Experts for Large Vision Language Models
by: Wang, Dianyi, et al.
Published: (2025)
by: Wang, Dianyi, et al.
Published: (2025)
Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
by: Li, Yunxin, et al.
Published: (2024)
by: Li, Yunxin, et al.
Published: (2024)
I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts
by: Xin, Jiayi, et al.
Published: (2025)
by: Xin, Jiayi, et al.
Published: (2025)
MoME: Mixture of Multimodal Experts for Cancer Survival Prediction
by: Xiong, Conghao, et al.
Published: (2024)
by: Xiong, Conghao, et al.
Published: (2024)
A Survey on Interpretability in Visual Recognition
by: Wan, Qiyang, et al.
Published: (2025)
by: Wan, Qiyang, et al.
Published: (2025)
VisKnow: Constructing Visual Knowledge Base for Object Understanding
by: Yao, Ziwei, et al.
Published: (2025)
by: Yao, Ziwei, et al.
Published: (2025)
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
by: Lin, Bin, et al.
Published: (2024)
by: Lin, Bin, et al.
Published: (2024)
Aria: An Open Multimodal Native Mixture-of-Experts Model
by: Li, Dongxu, et al.
Published: (2024)
by: Li, Dongxu, et al.
Published: (2024)
R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning
by: Guo, Xiaohan, et al.
Published: (2025)
by: Guo, Xiaohan, et al.
Published: (2025)
GEAR: GEometry-motion Alternating Refinement for Articulated Object Modeling with Gaussian Splatting
by: Li, Jialin, et al.
Published: (2026)
by: Li, Jialin, et al.
Published: (2026)
GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Experts
by: Liao, Minwen, et al.
Published: (2025)
by: Liao, Minwen, et al.
Published: (2025)
MoRE: 3D Visual Geometry Reconstruction Meets Mixture-of-Experts
by: Gao, Jingnan, et al.
Published: (2025)
by: Gao, Jingnan, et al.
Published: (2025)
Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation
by: Vu, Nguyen Lan Vi, et al.
Published: (2025)
by: Vu, Nguyen Lan Vi, et al.
Published: (2025)
OpenSubject: Leveraging Video-Derived Identity and Diversity Priors for Subject-driven Image Generation and Manipulation
by: Liu, Yexin, et al.
Published: (2025)
by: Liu, Yexin, et al.
Published: (2025)
TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts
by: Xu, Yu, et al.
Published: (2026)
by: Xu, Yu, et al.
Published: (2026)
MoE3D: A Mixture-of-Experts Module for 3D Reconstruction
by: Wang, Zichen, et al.
Published: (2026)
by: Wang, Zichen, et al.
Published: (2026)
Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models
by: Wang, Peiran, et al.
Published: (2025)
by: Wang, Peiran, et al.
Published: (2025)
MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal Fusion
by: Jiang, Ruixiang, et al.
Published: (2024)
by: Jiang, Ruixiang, et al.
Published: (2024)
Mixture-of-Modality-Experts with Holistic Token Learning for Fine-Grained Multimodal Visual Analytics in Driver Action Recognition
by: Liu, Tianyi, et al.
Published: (2026)
by: Liu, Tianyi, et al.
Published: (2026)
ViMoE: An Empirical Study of Designing Vision Mixture-of-Experts
by: Han, Xumeng, et al.
Published: (2024)
by: Han, Xumeng, et al.
Published: (2024)
MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification
by: Xu, Yichu, et al.
Published: (2025)
by: Xu, Yichu, et al.
Published: (2025)
GS-LTS: 3D Gaussian Splatting-Based Adaptive Modeling for Long-Term Service Robots
by: Fu, Bin, et al.
Published: (2025)
by: Fu, Bin, et al.
Published: (2025)
MoME: Mixture of Visual Language Medical Experts for Medical Imaging Segmentation
by: Rezvani, Arghavan, et al.
Published: (2025)
by: Rezvani, Arghavan, et al.
Published: (2025)
CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering
by: Huai, Tianyu, et al.
Published: (2025)
by: Huai, Tianyu, et al.
Published: (2025)
RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation
by: Bi, Hanbo, et al.
Published: (2025)
by: Bi, Hanbo, et al.
Published: (2025)
Similar Items
-
MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge Transfer
by: Zhu, Minghao, et al.
Published: (2024) -
Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment
by: Liu, Zhili, et al.
Published: (2024) -
Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models
by: Romero, Miguel, et al.
Published: (2025) -
MoTE: Mixture of Task-specific Experts for Pre-Trained ModelBased Class-incremental Learning
by: Li, Linjie, et al.
Published: (2025) -
M4U: Evaluating Multilingual Understanding and Reasoning for Large Multimodal Models
by: Wang, Hongyu, et al.
Published: (2024)