Different Modality

Multimodal learning focuses on integrating information from diverse data sources (e.g., text, images, audio) to improve model performance and robustness. Current research emphasizes efficient fusion techniques, addressing challenges like missing modalities through methods such as contrastive learning, modality-aware adaptation, and progressive alignment using lightweight architectures like OneEncoder. This field is significant for advancing AI capabilities in various applications, including medical diagnosis, visual question answering, and human activity recognition, by enabling more comprehensive and reliable analysis of complex data.