DecoMoE: Decoupling Visual Propagation and Expert Computation for Efficient Multimodal MoE Inference
cs.CV
Submitted: 2026-09-30
Updated: 2026-09-30
Code: https://github.com/ShawnTan86/DecoMoE
Terminology
Sources
- Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
- Qwen3-VL Technical Report
- Mixture Compressor for Mixture-of-Experts LLMs Gains More
- MoDES: Accelerating Mixture-of-Experts Multimodal Large Language Models via Dynamic Expert Skipping
- Mixtral of Experts
- Multi-Stage Vision Token Dropping: Towards Efficient Multimodal Large Language Model
- LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
- TokenCarve: Information-Preserving Visual Token Compression in Multimodal Large Language Models
- Kimi-VL Technical Report
- Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
- InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
- DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding
- FastMMoE: Accelerating Multimodal Large Language Models through Dynamic Expert Activation and Routing-Aware Token Pruning
- MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router
- SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
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