Leopard: A multimodal large language model (MLLM) designed specifically to handle vision and language tasks involving multiple text-rich images

In recent years, multimodal large language models (MLLM) have revolutionized vision-language tasks, improving capabilities such as image captioning and object detection. However, when it comes to multiple text-rich images, even the most modern models face significant challenges. The real-world need to understand and reason about text-rich images is crucial for applications such as processing presentation slides, scanned documents, and web page snapshots. Existing MLLMs, such as LLaVAR and mPlug-DocOwl-1.5, often fall short in handling these types of tasks, mainly due to two main problems: the lack of high-quality instruction tuning data sets specific for multiple images and the difficulty in maintaining an optimal balance between image resolution and visual sequence length. Addressing these challenges is vital to advancing real-world use cases where text-rich content plays a central role.

Researchers from the University of Notre Dame, Tencent ai Seattle Lab, and the University of Illinois Urbana-Champaign (UIUC) have introduced Leopard: a multimodal large language model (MLLM) designed specifically to handle vision and language tasks involving multiple rich images. in text. . Leopard aims to fill the gap left by current models and focuses on improving performance in scenarios where understanding the relationships and logical flows between multiple images is key. By curating a data set of approximately one million high-quality multimodal instruction tuning data points tailored to multi-image and text-rich scenarios, Leopard has a unique advantage. This extensive data set covers domains such as multi-page documents, tables and charts, and web snapshots, helping Leopard effectively handle complex visual relationships spanning multiple images. Additionally, Leopard incorporates an adaptive high-resolution multi-image encoding module, which dynamically optimizes visual sequence length mapping based on the original aspect ratios and resolutions of the input images.

Leopard introduces several advancements that distinguish it from other MLLMs. One of its most notable features is the adaptive high-resolution multiple image encoding module. This module allows Leopard to maintain high-resolution details while managing sequence lengths efficiently, avoiding the loss of information that occurs when over-compressing visual features. Instead of reducing resolution to fit model constraints, Leopard's adaptive encoding dynamically optimizes the mapping of each image, preserving crucial details even when dealing with multiple images. This approach allows Leopard to process text-rich images, such as scientific reports, without losing accuracy due to poor image resolution. By employing pixel mixing, Leopard can compress long sequences of visual features into shorter, lossless sequences, significantly improving its ability to handle complex visual input without compromising visual detail.

The importance of Leopard becomes even more evident when considering the practical use cases it addresses. In scenarios involving multiple text-rich images, Leopard substantially outperforms previous models such as OpenFlamingo, VILA, and Idefics2, which struggled to generalize across interrelated visual and textual inputs. Benchmarks showed that Leopard outperformed its competitors by a wide margin, achieving an average improvement of more than 9.61 points in key multi-image, text-rich benchmark tests. For example, on tasks like SlideVQA and multi-page DocVQA, which require reasoning about multiple interconnected visual elements, Leopard consistently generated correct answers where other models failed. This capability has immense value in real-world applications, such as understanding multi-page documents or analyzing presentations, which are essential in business, education, and research environments.

Leopard represents an important step forward for multimodal ai, particularly for tasks involving multiple text-rich images. By addressing the challenges of limited instruction tuning data and balancing image resolution with sequence length, Leopard offers a robust solution that can process complex, interconnected visual information. Its superior performance on several benchmarks, combined with its innovative approach to high-resolution adaptive encoding, underscores its potential impact on numerous real-world applications. As Leopard continues to evolve, it sets a promising precedent for the development of future MLLMs that can better understand, interpret, and reason across diverse multimodal inputs.

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Aswin AK is a Consulting Intern at MarkTechPost. He is pursuing his dual degree from the Indian Institute of technology Kharagpur. He is passionate about data science and machine learning, and brings a strong academic background and practical experience solving real-life interdisciplinary challenges.