Cover Image

CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Huai, Tianyu, Zhou, Jie, Wu, Xingjiao, Chen, Qin, Bai, Qingchun, Zhou, Ze, He, Liang
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Task-Core Memory Management and Consolidation for Long-term Continual Learning
by: Huai, Tianyu, et al.
Published: (2025)

RS-MoE: A Vision-Language Model with Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering
by: Lin, Hui, et al.
Published: (2024)

Dynamic Multimodal Activation Steering for Hallucination Mitigation in Large Vision-Language Models
by: Yin, Jianghao, et al.
Published: (2026)

I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts
by: Xin, Jiayi, et al.
Published: (2025)

One-for-All Does Not Work! Enhancing Vulnerability Detection by Mixture-of-Experts (MoE)
by: Yang, Xu, et al.
Published: (2025)

VA-MoE: Variables-Adaptive Mixture of Experts for Incremental Weather Forecasting
by: Chen, Hao, et al.
Published: (2024)

Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
by: Li, Yunxin, et al.
Published: (2024)

CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
by: Liu, Yang, et al.
Published: (2026)

SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts
by: Muzio, Alexandre, et al.
Published: (2024)

MoE-CE: Enhancing Generalization for Deep Learning based Channel Estimation via a Mixture-of-Experts Framework
by: Li, Tianyu, et al.
Published: (2025)

QA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of Experts for Robust Multimodal Sentiment Analysis
by: Zhu, Yitong, et al.
Published: (2026)

Horseshoe Mixtures-of-Experts (HS-MoE)
by: Polson, Nick, et al.
Published: (2026)

MoE Jetpack: From Dense Checkpoints to Adaptive Mixture of Experts for Vision Tasks
by: Zhu, Xingkui, et al.
Published: (2024)

CoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question Answering
by: Zeng, Xiyin, et al.
Published: (2026)

Enhancing Event Causality Identification with Rationale and Structure-Aware Causal Question Answering
by: Zhang, Baiyan, et al.
Published: (2024)

$\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts
by: Takashiro, Shota, et al.
Published: (2026)

Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts
by: Luo, Shuqing, et al.
Published: (2024)

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs
by: Zhang, Ze Yu, et al.
Published: (2025)

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
by: Tang, Yehui, et al.
Published: (2025)

Unified Multimodal Visual Tracking with Dual Mixture-of-Experts
by: Hong, Lingyi, et al.
Published: (2026)

FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing
by: Zhang, Boyang, et al.
Published: (2025)

MH-MoE: Multi-Head Mixture-of-Experts
by: Huang, Shaohan, et al.
Published: (2024)

MoE-Loco: Mixture of Experts for Multitask Locomotion
by: Huang, Runhan, et al.
Published: (2025)

Point-MoE: Large-Scale Multi-Dataset Training with Mixture-of-Experts for 3D Semantic Segmentation
by: Chen, Xuweiyi, et al.
Published: (2025)

MoE-Health: A Mixture of Experts Framework for Robust Multimodal Healthcare Prediction
by: Wang, Xiaoyang, et al.
Published: (2025)

MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)

MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs
by: Chen, Xiaodong, et al.
Published: (2025)

EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models
by: Chen, Yuanteng, et al.
Published: (2025)

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
by: Lin, Bin, et al.
Published: (2024)

LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training
by: Zhu, Tong, et al.
Published: (2024)

Mitigating Strategy Preference Bias in Emotional Support Conversation via Uncertainty Estimations
by: Zhou, Yougen, et al.
Published: (2025)

DIVE into MoE: Diversity-Enhanced Reconstruction of Large Language Models from Dense into Mixture-of-Experts
by: Feng, Yuchen, et al.
Published: (2025)

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts
by: Xue, Heyang, et al.
Published: (2025)

MoE-nD: Per-Layer Mixture-of-Experts Routing for Multi-Axis KV Cache Compression
by: Sun, Libo, et al.
Published: (2026)

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
by: Shi, Xiaoming, et al.
Published: (2024)

An Entailment Tree Generation Approach for Multimodal Multi-Hop Question Answering with Mixture-of-Experts and Iterative Feedback Mechanism
by: Zhang, Qing, et al.
Published: (2024)

Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models
by: Wei, Tianwen, et al.
Published: (2024)

Mixture of Experts (MoE): A Big Data Perspective
by: Gan, Wensheng, et al.
Published: (2025)

SDG-MoE: Signed Debate Graph Mixture-of-Experts
by: Kulibaba, Stepan, et al.
Published: (2026)

ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
by: Xu, Yuhao, et al.
Published: (2026)