Unimodal Representation

Unimodal representation focuses on extracting meaningful information from single data modalities (e.g., images, text, audio) before integrating them in multimodal systems. Current research emphasizes improving the quality of these unimodal representations through techniques like transformer-based architectures, contrastive learning, and prompt engineering, aiming to address issues such as semantic imbalance and noise. This work is crucial for advancing multimodal learning, as robust and informative unimodal representations are foundational for effective cross-modal fusion and improved performance in applications like sentiment analysis, action anticipation, and few-shot learning. The development of more effective unimodal representations directly impacts the accuracy and efficiency of various multimodal AI systems.