With the continuous development of artificial intelligence technology, the integration of visual and textual data has become a complex challenge. Traditional models often struggle to accurately parse structured visual documents such as tables, charts, infographics, and diagrams. This limitation impacts automated content extraction and comprehension capabilities, subsequently affecting applications in data analysis, information retrieval, and decision-making. In response to this demand, IBM recently released Granite-Vision-3.1-2B, a compact visual language model specifically designed for document understanding.

Recently, Hugging Face partnered with Physical Intelligence to launch 'Pi0' (Pi-Zero), the first foundational model that directly converts natural language commands into physical actions. This innovative release has garnered widespread attention, with Hugging Face's Chief Research Scientist Remi Cadene announcing on social media, 'Pi0 is the most advanced visual-language action model that can translate natural language commands into autonomous behaviors.'

With the rapid development of artificial intelligence, the integration of visual and language capabilities has led to groundbreaking advancements in visual language models (VLMs). These models aim to simultaneously process and understand both visual and textual data, being widely applied in scenarios such as image captioning, visual question answering, optical character recognition, and multimodal content analysis. VLMs play a significant role in developing autonomous systems, enhancing human-computer interaction, and creating efficient document processing tools, successfully bridging the gap between these two data modalities. However, there are challenges in handling high-resolution visual data and diverse textual input.