IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models

1. 1Reformulate attention mechanism in dual form perspective
2. 2Identify redundant visual tokens using implicit weight pruning
3. 3Prune redundant tokens to reduce computational cost
4. 4Evaluate model performance after pruning

AI engineers and researchers working on large vision language models can benefit from this framework to improve model efficiency and reduce computational costs

💡 Token pruning can be achieved through implicit weight pruning without requiring retraining

💡 Novel token pruning framework for large vision language models reduces computational cost without retraining!