Multimodal Vision Language Model

Multimodal vision-language models (VLMs) aim to integrate visual and textual information, enabling computers to understand and generate descriptions of images. Current research focuses on improving VLM performance in challenging scenarios, such as handling occluded objects, generating diverse and non-generic text, and adapting to low-resource languages, often leveraging architectures like CLIP and transformer-based models. These advancements are significant because they enhance the capabilities of AI systems in various applications, including image retrieval, graphic design, and human-robot interaction, while also raising important considerations regarding privacy and robustness.