Large Scale Vision Language

Large-scale vision-language (V-L) models aim to integrate visual and textual information for improved understanding and generation of multimodal data. Current research focuses on adapting these pre-trained models to specific tasks, such as robotic control and anomaly detection, often through techniques like prompt tuning and the insertion of concept-aware adapters to enhance performance and mitigate biases. This field is significant because it enables more robust and versatile AI systems capable of interacting with the world in a more human-like way, with applications ranging from assistive robotics to improved image understanding and generation.