VeLAR: Vision-oriEnted Language-Attentive token Reduction for multimodal large language models

We propose a token reduction framework for MLLMs that reduces vision token redundancy in vision-language learning, cutting computational costs by up to 42% while maintaining and even surpassing the original model performance.