arXiv:2604.00757v2 Announce Type: replace-cross Abstract: Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidly with the number of visual tokens. Existing token pruning methods mitigate this issue through empirical approaches while overlooking the internal mechanism of attention. In this paper, we propose a novel training free token pruning framework grounded in the dual form perspective of attention. We reformulate attention as an implicit linear layer whose weight matrix is the sum of rank 1 outer produ
The increasing scale of Large Vision Language Models (LVLMs) is pushing the boundaries of computational resources, creating an urgent need for more efficient processing techniques.
This development offers a novel, training-free approach to accelerate LVLMs by optimizing token processing, directly addressing the significant computational cost that limits their deployment and scalability.
The proposed 'Implicit Weight Pruning' (IWP) framework changes how attention mechanisms are optimized in LVLMs, moving from empirical token pruning to a more theoretically grounded and efficient method.
- · AI compute providers
- · Developers of Large Vision Language Models
- · Sectors adopting advanced AI vision capabilities
- · Cloud computing platforms
- · Companies reliant on brute-force computational scaling for LVLMs
- · Existing less-efficient token pruning methods
LVLMs become more computationally efficient, leading to faster inference and training times.
Reduced operational costs for deploying and running sophisticated AI vision applications, broadening accessibility and adoption.
Accelerated development of more complex and robust multi-modal AI systems as computational constraints are eased, potentially impacting various industries from autonomous vehicles to medical imaging.
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Read at arXiv cs.AI