Modality Combination

Modality combination in machine learning focuses on effectively integrating information from diverse data sources (e.g., images, text, audio) to improve model performance and robustness. Current research emphasizes handling missing modalities at inference time, developing flexible architectures that adapt to varying combinations of input data, and employing techniques like knowledge distillation and representation decoupling to enhance learning efficiency and generalization. This field is crucial for advancing AI systems that can operate reliably in real-world scenarios with incomplete or variable sensory inputs, impacting applications ranging from medical diagnosis to robotics.