Efficient Vision Language Model

Efficient Vision-Language Models (VLMs) aim to improve the speed and resource efficiency of models that understand and process both visual and textual information, crucial for applications like autonomous driving and CAD design. Current research focuses on optimizing existing architectures like transformers through techniques such as token sparsification, pruning, and the use of Mixture of Experts (MoE) to reduce computational cost while maintaining accuracy. These advancements are significant because they enable deployment of powerful VLMs on resource-constrained devices and improve the speed of real-time applications, broadening the accessibility and applicability of multimodal AI.