Cross Modal Knowledge

Cross-modal knowledge research focuses on leveraging information from multiple data modalities (e.g., text, images, audio, video) to improve the performance of AI systems. Current research emphasizes developing models and algorithms, such as contrastive learning and various adaptations of BERT architectures, that effectively integrate and reason across these different data types, often addressing challenges like knowledge conflicts between modalities and data scarcity in certain domains. This field is significant because it enables the creation of more robust and intelligent systems capable of understanding complex real-world scenarios, with applications ranging from improved object detection and activity recognition to more sophisticated question answering systems.