Autoregressive Vision Language Model

Autoregressive vision-language models (VLMs) aim to create AI systems that understand and generate both visual and textual information seamlessly. Current research focuses on improving their instruction-following abilities, addressing vulnerabilities like backdoor attacks, and developing more efficient architectures, such as contrastive models with streamlined multimodal components, to handle longer text contexts and diverse data types. These advancements are significant because they enable more robust and versatile AI systems with applications ranging from image captioning and generation to complex multimodal question answering, impacting fields like computer vision and natural language processing.