1 citations · 1 across the 8 of their papers we have counts for
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C-PTQ: Fisher-weighted Channel-wise Sensitivity for Post-training Quantization of MLLMs
Jiameng Li, Han Zhou, Matthew B. Blaschko
Multimodal large language models (MLLMs) require huge memory and computational costs, which limits their practical deployment. Post-training quantization (PTQ) techniques offer an…
EchoPrune: Interpreting Redundancy as Temporal Echoes for Efficient VideoLLMs
Jiameng Li, Minye Wu, Jiezhang Cao +2
Long-form video understanding remains challenging for Video Large Language Models (VideoLLMs), as the dense frame sampling introduces massive visual tokens while sparse sampling ri…
MI-Pruner: Crossmodal Mutual Information-guided Token Pruner for Efficient MLLMs
Jiameng Li, Aleksei Tiulpin, Matthew B. Blaschko
For multimodal large language models (MLLMs), visual information is relatively sparse compared with text. As a result, research on visual pruning emerges for efficient inference. C…