VisCo framework uses LLMs for efficient visual token compression

Researchers have introduced VisCo, a novel framework designed to compress visual tokens in vision-language models (VLMs) by utilizing the VLM itself as an intrinsic encoder. This self-compression approach reuses pretrained VLM capabilities to reduce inference latency and memory overhead without requiring extensive retraining or external modules. Experiments demonstrate that VisCo achieves superior performance across various compression ratios, even outperforming prior methods at extreme compression levels and showing potential to enhance base models by capturing complementary representations. AI

IMPACT This method could significantly reduce the computational cost of deploying vision-language models, making them more accessible and efficient.

RANK_REASON The item is a research paper detailing a new method for visual token compression in vision-language models. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• large-language models
• ScienceCast
• VisCo
• vision-language model
• Vision--Language Models

• paper
• infra

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