Peking University and Alibaba Group developed FastV to tackle inefficiencies in Large Vision-Language Models’ attention computation. FastV dynamically prunes less relevant visual tokens, significantly reducing computational costs without compromising performance. This improves the computational efficiency and practical deployment of LVLMs, offering a promising solution to resource constraints in real-world applications.
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FastV: A Plug-and-Play Inference Acceleration AI Method for Large Vision Language Models Relying on Visual Tokens
Researchers from Peking University and Alibaba Group have introduced FastV to address challenges in Large Vision-Language Models (LVLMs) caused by inefficient attention computation. Existing models such as LLaVA-1.5 and Video-LLaVA have shown advancements in LVLMs, but struggle with the bottleneck in the attention mechanism, particularly concerning the handling of visual tokens. The attention mechanism within LVLMs exhibits a bias towards textual tokens, resulting in inefficient utilization of visual information.
Practical Solution: FastV
FastV is a dynamic pruning method designed to optimize computational efficiency in LVLMs. It addresses the issue of inefficient attention computation by introducing a dynamic pruning mechanism for visual tokens during the inference phase of LVLMs. This selective pruning strategy significantly reduces the computational burden of LVLMs, particularly in deep layers, while maintaining superior performance across various vision-language tasks.
FastV’s flexibility allows users to customize the trade-off between computational efficiency and performance according to their specific requirements, making it a versatile and practical solution for deploying LVLMs in resource-constrained environments.
Value and Practical Deployment
FastV has shown significant effectiveness in precisely targeting image tokens for reduction, thereby optimizing performance without compromising the model’s overall functionality. It represents a significant step towards improving the computational efficiency and practical deployment of LVLMs, offering a promising solution to the challenges posed by resource constraints in real-world applications.
For more information, check out the Paper and Github.
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