Multimodal BERT

Multimodal BERT models extend the capabilities of the original BERT architecture by integrating multiple data modalities, such as text, images, and audio, to improve understanding and representation learning. Current research focuses on developing efficient fusion methods (e.g., layer-wise fusion, disentangled attention) and effective pre-training strategies (e.g., contrastive learning, cross-modal self-supervised tasks) to leverage the combined information. These advancements are significantly impacting various applications, including sentiment analysis, visual-language tasks, and e-commerce retrieval, by enabling more robust and accurate models that outperform unimodal approaches.