Multimodal Large Language Models (MLLMs) suffer from high computational costs
due to their massive size and the large number of visual tokens. In this paper,
we investigate layer-wise redundancy in MLLMs by introducing a novel metric,
Layer Contribution (LC), which quantifies the impact of a layer’s
transformations on visual and text tokens, respectively. The calculation of LC
involves measuring the divergence in model output that results from removing
the layer’s transformations on the specified tokens. Our pilot experiment
reveals that many layers of MLLMs exhibit minimal contribution during the
processing of visual tokens. Motivated by this observation, we propose ShortV,
a training-free method that leverages LC to identify ineffective layers, and
freezes visual token updates in these layers. Experiments show that ShortV can
freeze visual token in approximately 60\% of the MLLM layers, thereby
dramatically reducing computational costs related to updating visual tokens.
For example, it achieves a 50\% reduction in FLOPs on LLaVA-NeXT-13B while
maintaining superior performance. The code will be publicly available at
https://github.com/icip-cas/ShortV
