Scaling Vision-Language Models with Sparse Mixture of Experts

Understand

The field of natural language processing (NLP) has made significant strides in recent years, particularly in the development of large-scale vision-language models (VLMs).

• These models aim to bridge the gap between text and visual information, enabling a more comprehensive understanding of multimedia data.
• However, as these models become larger and more complex, they also become more challenging to train and deploy.
• One approach to addressing this challenge is the use of sparsely-gated mixture-of-experts (MoE) techniques, which divide the model into smaller, specialized sub-models that can jointly solve a task.