VEGA: Learning Interleaved Image-Text Comprehension in Vision-Language Large Models

TL;DR

This paper introduces the Interleaved Image-Text Comprehension (IITC) task and the VEGA dataset to evaluate and enhance vision-language models' ability to handle complex, misleading, and interleaved visual and textual information.

Contribution

The paper presents a new challenging IITC task, a specialized VEGA dataset for scientific content, and a multi-task training strategy to improve models' nuanced image-text comprehension capabilities.

Findings

Even top models like GPT4V achieve modest success on IITC.

Multi-task, multi-scale post-training improves image association accuracy to 85.8%.

The VEGA dataset effectively benchmarks and enhances MLLMs' comprehension skills.

Abstract

The swift progress of Multi-modal Large Models (MLLMs) has showcased their impressive ability to tackle tasks blending vision and language. Yet, most current models and benchmarks cater to scenarios with a narrow scope of visual and textual contexts. These models often fall short when faced with complex comprehension tasks, which involve navigating through a plethora of irrelevant and potentially misleading information in both text and image forms. To bridge this gap, we introduce a new, more demanding task known as Interleaved Image-Text Comprehension (IITC). This task challenges models to discern and disregard superfluous elements in both images and text to accurately answer questions and to follow intricate instructions to pinpoint the relevant image. In support of this task, we further craft a new VEGA dataset, tailored for the IITC task on scientific content, and devised a subtask, Image-Text Association (ITA), to refine image-text correlation skills. Our evaluation of four leading closed-source models, as well as various open-source models using VEGA, underscores the rigorous nature of IITC. Even the most advanced models, such as Gemini-1.5-pro and GPT4V, only achieved modest success. By employing a multi-task, multi-scale post-training strategy, we have set a robust baseline for MLLMs on the IITC task, attaining an $85.8\%$ accuracy rate in image association and a $0.508$ Rouge score. These results validate the effectiveness of our dataset in improving MLLMs capabilities for nuanced image-text comprehension.