  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.
