Multi Modal Understanding

Multi-modal understanding focuses on enabling AI systems to comprehend and interact with information presented across multiple modalities, such as text, images, and audio, mirroring human cognitive abilities. Current research emphasizes developing large language models (LLMs) with enhanced multi-modal capabilities, often employing architectures like transformers and diffusion models, and focusing on improving tasks such as visual question answering, video-text retrieval, and complex visual reasoning. This field is crucial for advancing AI's ability to interact with the real world, impacting applications ranging from healthcare diagnostics to more intuitive human-computer interaction. The development of large, diverse datasets is also a key area of focus to support the training of these increasingly complex models.