Adaptive Visual Token Pruning for Long Context LMMs

This paper addresses the computational cost issue in Large Multimodal Models (LMMs) when dealing with long context and multiple images. It proposes a novel adaptive pruning method, TrimTokenator-LC, that considers both intra-image and inter-image redundancy to reduce the number of visual tokens while maintaining performance. This is significant because it tackles a practical bottleneck in the application of LMMs, especially in scenarios involving extensive visual information.