DeepSuM introduces a novel framework for efficient modality selection in multimodal learning systems, addressing the critical balance between performance and resource utilization.

• Intelligently selects essential modalities based on their contribution to overall task performance
• Reduces computational overhead while maintaining robust model accuracy
• Applicable across diverse fields including healthcare, robotics, and large language models
• Creates adaptive systems that allocate resources more efficiently

For healthcare applications, this framework enables more resource-efficient diagnostic and monitoring systems that can function effectively in varied clinical settings with different hardware constraints.

DeepSuM: Deep Sufficient Modality Learning Framework