Unraveling the Shift of Visual Information Flow in MLLMs: From Phased Interaction to Efficient Inference
Keywords: Multimodal Large Language Models, Visual Information Flow, Inference Acceleration
TL;DR: This paper uncovers a shift in visual information processing in MLLMs and introduces a novel image token pruning method to accelerate inference.
Abstract: Multimodal large language models (MLLMs) improve performance on vision-language tasks by integrating visual features from pre-trained vision encoders into large language models (LLMs). However, how MLLMs process and utilize visual information remains unclear. In this paper, a shift in the dominant flow of visual information is uncovered: (1) in shallow layers, strong interactions are observed between image tokens and instruction tokens, where most visual information is injected into instruction tokens to form cross-modal semantic representations; (2) in deeper layers, image tokens primarily interact with each other, aggregating the remaining visual information to optimize semantic representations within the visual modality. Based on these insights, we propose Hierarchical Modality-Aware Pruning (HiMAP), a plug-and-play inference acceleration method that dynamically prunes image tokens at specific layers, reducing computational costs by approximately 65% without sacrificing performance. Our findings offer a new understanding of visual information processing in MLLMs and provide a state-of-the-art solution for efficient inference. Code is released at https://anonymous.4open.science/r/HiMAP.
Primary Area: interpretability and explainable AI
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Submission Number: 9526
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