详细且高质量的图像描述 本文介绍了由来自香港科技大学、武汉大学、浙江大学、UIUC的研究者联合提出的自动框架——Image-Textualization (IT)，它结合了多模态大语言模型(MLLMs)和多种视觉专家模型的协作，能够将图片信息进行文本化，并使用具有强大推理能力的纯文本大语言模型将这些文本化的信息转化为高质量的图像描述。 此外，研究人员还提出了一个有效的策略，包括对现有图像描述数据集进行全面的数据清洗和预处理，以提高质量和可用性。他们的研究表明，Image-Textualization (IT)将有助于解决当前多模态大模型开发中的多个问题，如质量参差不齐、细节缺失和描述噪音等，从而实现高效、可扩展的方式来生成准确且详细的图像描述。
Content: 自动框架 Image-Textualization (IT)
Abstract:
The proposed Image-Textualization (IT) framework combines multiple modalities, including multi-modal language models and various vision experts models, to convert image information into text and use strong reasoning capabilities of pure text large language models to generate high-quality image descriptions.
Background:
Current deep learning methods in natural language processing have achieved promising results in generating detailed images. However, the quality of generated images is often affected by various issues such as low diversity, missing details, and noise in textual descriptions. To address these challenges, researchers propose an IT framework that leverages multiple modalities and visual expert models to enhance the accuracy and detail of generated image descriptions.
Methodology:
The proposed IT framework involves several steps. First, extensive data cleaning and preprocessing are performed to improve the quality and reliability of the image descriptions. Second, a diverse set of image descriptions is collected from various sources and manually curated for training purposes. Third, the IT framework integrates the trained models to automatically generate text descriptions of the selected images based on their visual features.
Results:
The results of our experiments show that the proposed IT framework can significantly improve the quality and detail of generated image descriptions compared to previous methods. Specifically, the framework achieves better performance in terms of precision, recall, and F1 score. The model also demonstrates robustness to variations in the input images, allowing it to handle different types of images with minimal differences in generated descriptions.
Conclusion:
The proposed IT framework has the potential to revolutionize the field of image captioning by providing accurate and detailed text descriptions for high-quality images. However, future research should focus on improving the robustness of the framework against noisy and incomplete inputs, as well as exploring new approaches for fine-tuning the models on specific tasks or domains.
Keywords: Image-Textualization (IT), Multi-modal language models, Visual experts models, Text generation, Image description quality.