Large Vision Language

Large vision-language models (VLMs) aim to integrate visual and textual information, enabling computers to understand and reason about images and text simultaneously. Current research focuses on improving VLM performance in challenging scenarios, such as handling occluded objects in images and extending capabilities to longer videos and more complex tasks like chart comprehension. This involves developing novel architectures, efficient fine-tuning techniques, and large-scale datasets to address limitations in existing models. Advances in VLMs have significant implications for various applications, including robotics, image retrieval, and question answering systems.