Multimodal Distillation

Multimodal distillation focuses on transferring knowledge learned from multiple data modalities (e.g., images, text, audio) to a more efficient or robust model, often addressing challenges like data scarcity, computational cost, or domain adaptation. Current research emphasizes developing novel distillation techniques, including modality-aware and decoupled approaches, often incorporating transformer-based architectures or leveraging pre-trained foundation models like CLIP. This research is significant because it enables the development of high-performing multimodal systems even with limited data or resources, impacting fields such as visual question answering, emotion recognition, and medical image analysis.