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New C2-Routing method boosts VLM efficiency by 36%

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called C$^2$-Routing to improve the efficiency of vision-language models (VLMs) during inference. This technique adaptively combines image cropping and compression to reduce computational load without significantly sacrificing accuracy. C$^2$-Routing aims to optimize VQA tasks by intelligently selecting the best input reduction strategy based on the image content and the question being asked. Experiments show it can reduce FLOPs by over 36% and visual tokens by over 42% on average, with minimal impact on task performance. AI

IMPACT This method could lead to more efficient deployment of vision-language models, reducing computational costs for applications like visual question answering.

RANK_REASON The cluster describes a new method presented in an arXiv paper for improving the efficiency of vision-language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New C2-Routing method boosts VLM efficiency by 36%

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The cluster describes a new method presented in an arXiv paper for improving the efficiency of vision-language models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Guorui Lu, Qinyu Chen ·

    C$^2$-Routing: Complementary Cropping and Compression for Efficient Vision-Language Inference

    arXiv:2611.14469v1 Announce Type: cross Abstract: Visual Question Answering (VQA) imposes substantial computational overhead on vision-language models (VLMs), motivating model-agnostic input reduction methods that can be readily applied across diverse VLMs, including in black-box…

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