Large Scale Multimodal

Large-scale multimodal research focuses on developing models that can understand and generate information across multiple data types (e.g., text, images, audio, video). Current efforts concentrate on creating universal embedding models, often using transformer-based architectures and contrastive learning, to handle diverse downstream tasks like visual question answering and root cause analysis. These advancements are driving progress in various fields, including improved document analysis, more effective misinformation detection, and enhanced human-computer interaction through more natural and nuanced conversational AI. The availability of large, diverse, and publicly accessible multimodal datasets is crucial to this progress.