MST-Distill: Mixture of Specialized Teachers for Cross-Modal Knowledge Distillation
Hui Li, Pengfei Yang, Juanyang Chen, Le Dong, Yanxin Chen, Quan Wang
2025-07-17
Summary
This paper talks about MST-Distill, a new method that improves how AI models learn from each other across different types of data like images and text by using a group of specialized teacher models and a smart way to pick the best guidance for each example.
What's the problem?
The problem is that when transferring knowledge between models working with different kinds of data, it’s hard to choose the right teacher for each example and to prevent losing important information during training.
What's the solution?
The authors developed MST-Distill which uses multiple specialized teacher models and a routing network that decides which teacher to learn from for each data point, making the learning process more precise and avoiding knowledge loss.
Why it matters?
This matters because it helps create AI that can better understand and connect information from different sources, improving performance on tasks like recognizing objects, understanding text, and other applications involving multiple types of data.
Abstract
MST-Distill, a novel cross-modal knowledge distillation framework, uses a mixture of specialized teachers and an instance-level routing network to address distillation path selection and knowledge drift, outperforming existing methods across multimodal datasets.