In recent years, large language models (LLMs) have gained prominence in artificial intelligence, but they have focused primarily on text and have struggled to understand visual content. Multimodal Large Language Models (MLLM) have emerged to bridge this gap. MLLMs combine visual and textual information into a single Transformer-based model, allowing them to learn and generate content from both modalities, marking a significant advancement in ai capabilities.
KOSMOS-2.5 is a multimodal model designed to handle two closely related transcription tasks within a unified framework. The first task is to generate spatially aware text blocks and assign spatial coordinates to text lines within text-rich images. The second task focuses on producing structured text in Markdown format, capturing various styles and structures.
Both tasks are managed under a single system, using a shared Transformer architecture, task-specific prompts, and adaptive text representations. The model architecture combines a vision encoder based on ViT (Vision Transformer) with a language decoder based on the Transformer architecture, connected through a resampler module.
To train this model, it is pre-trained on a substantial dataset of text-heavy images, including lines of text with bounding boxes and plain text. This dual-task training approach improves the overall multimodal literacy capabilities of KOSMOS-2.5.
The image above shows the model architecture of KOSMOS-2.5. The performance of KOSMOS-2.5 is evaluated through two main tasks: end-to-end document-level text recognition and text generation from images in Markdown format. Experimental results have demonstrated its strong performance in understanding text-intensive image tasks. Furthermore, KOSMOS-2.5 exhibits promising capabilities in scenarios involving few-shot and zero-shot learning, making it a versatile tool for real-world applications dealing with text-rich images.
Despite these promising results, the current model faces some limitations, offering valuable directions for future research. For example, KOSMOS-2.5 does not currently support fine-grained control of document element positions using natural language instructions, despite having been previously trained on inputs and outputs involving the spatial coordinates of the text. In the broader research landscape, an important direction lies in promoting the development of model scaling capabilities.
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Janhavi Lande, Graduated in Engineering Physics from IIT Guwahati, Class of 2023. She is an upcoming data scientist and has been working in the world of ml/ai research for the last two years. What fascinates him most is this ever-changing world and its constant demand for humans to keep up. In her hobbies she likes to travel, read and write poems.
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