Multimodal Chart

Multimodal chart research focuses on developing computational methods to understand and interact with charts containing diverse data types like text, images, and numerical data. Current efforts concentrate on leveraging large language models (LLMs) and vision transformers (ViTs) for tasks such as question answering, sentiment analysis, and information extraction from charts, often employing techniques like contrastive learning and knowledge distillation to improve model performance. This field is significant for advancing human-computer interaction and enabling more sophisticated analysis of complex visual data across various domains, including healthcare, scientific literature, and social media.