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Clustering and Token Denoising for Faster and More Robust VLMs
Not provided in the abstract
token pruningmultimodalrobustnessVLM
2608.19285
Builder Relevance
2h ago80%
Abstract
The paper introduces ClustRS, a training-free algorithm for robust token pruning in Visual-Language Models, enhancing their deployment in real-world scenarios.
Reality Card
Core Claim
ClustRS improves robustness and efficiency in Visual-Language Models by reducing the number of tokens processed by up to 97% while maintaining performance.
Method / Result
Achieved a reduction of tokens by 97%, down to 16 tokens, while matching baseline performance under mild noise conditions.
Limitations
The paper does not specify the authors or provide detailed experimental setups, which may hinder reproducibility.
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