PyramidDrop presents a novel technique to improve computational efficiency of Large Vision-Language Models by intelligently reducing visual token redundancy.

• Reduces computational costs that grow quadratically with image resolution
• Uses a pyramid-based approach to selectively drop redundant visual information
• Maintains model performance while significantly improving processing speed
• Addresses a key bottleneck in vision-language models where images require hundreds or thousands of tokens

This innovation has critical implications for engineering more efficient AI systems that process both visual and textual data, enabling faster training cycles and more responsive inference in production environments.

PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction