Unimodal Learning

Unimodal learning, focusing on training models using data from a single modality (e.g., only images or only text), is being actively investigated for its potential to improve efficiency and address data scarcity in multimodal learning. Current research explores techniques like layer-wise training and Pareto optimization to enhance resource efficiency and mitigate gradient conflicts when integrating unimodal learning into multimodal frameworks. These advancements are significant because they offer solutions to the computational and data challenges inherent in multimodal learning, leading to more robust and resource-efficient AI systems across various applications, including robotics and sentiment analysis.