Multimodal Information Extraction

Multimodal information extraction (MIE) focuses on automatically extracting structured information from data encompassing multiple modalities, such as text and images, aiming to overcome limitations of unimodal approaches. Current research emphasizes developing unified models capable of handling diverse tasks and datasets, often employing graph neural networks, contrastive learning, and instruction tuning to effectively fuse visual and textual information, and address modality gaps. These advancements are improving the accuracy and generalizability of information extraction from complex, visually-rich documents, with significant implications for applications in document understanding, social media analysis, and other fields requiring the processing of multimedia content.