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Liping Shan

2 accepted papers

2025

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

ACL 2025long

Large Vision-Language Models (LVLMs) have achieved remarkable success, yet their significant computational demands hinder practicaldeployment. While efforts to improve LVLM efficiency are growing, existing methods lack comprehensive evaluation across diverse backbones, benchmarks, and metrics. In th…

Cited by 0SourcePDFScholar
2024

SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models

COLING 2024main

Despite achieving remarkable performance on various vision-language tasks, Transformer-based Vision-Language Models (VLMs) suffer from redundancy in inputs and parameters, significantly hampering their efficiency in real-world applications. Moreover, the degree of redundancy in token representations…