Modality Representation

Modality representation focuses on effectively combining and utilizing information from diverse data sources (modalities) like images, audio, and text to improve the performance of machine learning models. Current research emphasizes robust fusion techniques, often employing contrastive learning and knowledge distillation to align and integrate representations across modalities, addressing challenges like missing data and modality gaps. This work is crucial for advancing applications in various fields, including healthcare (e.g., improved diagnosis prediction), autonomous driving (e.g., enhanced object detection), and sentiment analysis (e.g., more accurate emotion recognition), by enabling more accurate and reliable models from incomplete or heterogeneous data.