EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models

• URL: http://arxiv.org/abs/2506.00479v1
• Date: Sat, 31 May 2025 09:10:43 GMT
• Title: EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models
• Authors: Zekun Wang, Minghua Ma, Zexin Wang, Rongchuan Mu, Liping Shan, Ming Liu, Bing Qin,
• Abstract summary: Large Vision-Language Models (LVLMs) have achieved remarkable success, yet their significant computational demands hinder practical deployment.<n>We introduce EffiVLM-Bench, a unified framework for assessing not only absolute performance but also generalization and loyalty.<n>Our experiments and in-depth analyses offer insights into optimal strategies for accelerating LVLMs.
• Score: 19.344130974979503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable success, yet their significant computational demands hinder practical deployment. While efforts to improve LVLM efficiency are growing, existing methods lack comprehensive evaluation across diverse backbones, benchmarks, and metrics. In this work, we systematically evaluate mainstream acceleration techniques for LVLMs, categorized into token and parameter compression. We introduce EffiVLM-Bench, a unified framework for assessing not only absolute performance but also generalization and loyalty, while exploring Pareto-optimal trade-offs. Our extensive experiments and in-depth analyses offer insights into optimal strategies for accelerating LVLMs. We open-source code and recipes for EffiVLM-Bench to foster future research.

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