Multimodal Understanding

Multimodal understanding focuses on enabling machines to comprehend and integrate information from multiple sources like text, images, audio, and video, mirroring human cognitive abilities. Current research emphasizes developing large multimodal language models (MLLMs) using various architectures, including transformers and diffusion models, often incorporating techniques like instruction tuning and knowledge fusion to improve performance on diverse tasks. This field is crucial for advancing artificial general intelligence and has significant implications for applications ranging from robotics and human-computer interaction to scientific discovery and creative content generation.