MiCo method optimizes MLLM inference by reducing visual tokens

Researchers have developed MiCo, a novel training-free method to optimize multimodal large language model (MLLM) inference by reducing the number of visual tokens processed. MiCo employs a two-stage pruning approach that selects representative visual tokens based on semantic erasure modeling and task-aware subset selection. This method consistently outperforms existing techniques across various MLLMs and benchmarks, significantly improving inference speed while maintaining high performance. For instance, MiCo reduced visual tokens to 5.6% for LLaVA-NEXT-13B, achieving a 3.8-fold speedup with only a 2.5% drop in performance. AI

IMPACT This method could significantly reduce computational costs and improve the efficiency of multimodal AI systems.

RANK_REASON The cluster describes a new method presented in an arXiv paper for optimizing multimodal large language model inference. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• Core Recommendations for Antifungal Stewardship: A Statement of the Mycoses Study Group Education and Research Consortium
• DagsHub
• Gotit.pub
• Hugging Face
• LLaVA-NEXT-13B
• MiCo
• multimodal large language model
• ScienceCast
• Tinghao Wang

• paper
• infra

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