The Challenge of Deploying Multiple Vision Foundation Models

As the field of Artificial Intelligence continues to advance, the availability of pre-trained vision foundation models (VFMs) like CLIP, DINOv2, and SAM has grown. However, users often struggle with the storage, memory, and computational demands of deploying multiple models simultaneously. This can hinder the efficiency and effectiveness of AI applications.

A New Approach: Joint Distillation

To address these challenges, a unique approach called “joint distillation” has been developed. This method combines the capabilities of multiple VFMs into a single, efficient multi-task model. By integrating teacher-student learning with self-distillation, this approach can operate using just unlabeled image data and significantly reduce computational requirements compared to traditional multi-task learning.

The Benefits of Merging VFMs

In a recent demonstration merging CLIP and SAM, a new model called SAM-CLIP was created. This merged model not only retains the strengths of the original models but also uncovers new synergies, like text-prompted zero-shot segmentation. This highlights the potential for the joint distillation approach to streamline model deployment and enhance operational efficiency in the AI industry.