What are multimodal distillation techniques, and how are they applied?

Multimodal distillation techniques involve transferring knowledge from a complex, high-capacity model (the teacher) to a simpler model (the student) that is easier to deploy. In the context of multimodal AI, this can mean distilling knowledge from a large multimodal model that integrates text, images, and audio into a smaller model that can efficiently process and integrate these modalities. This is achieved by aligning the outputs of the teacher model with the student model during training, which enables the student to generalize better and make predictions across different modalities while being resource-efficient.