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cs.CV2024
Accelerating Multimodal Large Language Models via Dynamic Visual-Token Exit and the Empirical Findings
Qiong Wu, Wenhao Lin, Yiyi Zhou +4
The excessive use of visual tokens in existing Multimoal Large Language Models (MLLMs) often exhibits obvious redundancy and brings in prohibitively expensive computation. To gain…
cs.CV2024
Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models
Weihao Ye, Qiong Wu, Wenhao Lin +1
Recent progress in Multimodal Large Language Models(MLLMs) often use large image tokens to compensate the visual shortcoming of MLLMs, which not only exhibits obvious redundancy bu…
cs.MM2024
Not All Attention is Needed: Parameter and Computation Efficient Transfer Learning for Multi-modal Large Language Models
Qiong Wu, Weihao Ye, Yiyi Zhou +2
In this paper, we propose a novel parameter and computation efficient tuning method for Multi-modal Large Language Models (MLLMs), termed Efficient Attention Skipping (EAS). Concre…