高分辨率AI模型Griffon v2:通过文本和视觉提示提供灵活的对象引用 Griffon v2：如何利用文本和视觉提示实现灵活的对象引用

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How Can LVLMs Improve Text and Image Understanding Tasks?

Recent advancements in Large-Scale Vision Language Models (LVLMs) have shown great promise in tasks requiring text and image comprehension. Models like Griffon have excelled in tasks such as object detection, indicating significant progress in internal perception within LVLMs. This advancement has led to further research on flexible referencing beyond textual descriptions to enhance user interfaces.

What Limitations Do LVLMs Face in Complex Contexts?

Despite significant progress in fine-grained object perception, LVLMs face limitations in outperforming task-specific experts in complex scenarios due to image resolution constraints. These limitations hinder their ability to effectively reference objects using text and visual cues, particularly in areas like GUI agents and counting activities.

How Does Griffon v2 Overcome These Limitations?

To address these limitations, a group of researchers introduced Griffon v2, a unified high-resolution model designed to provide flexible object referencing through text and visual cues. They proposed a simple and lightweight downsampling projector to effectively increase image resolution, aiming to overcome constraints imposed by input tokens of large language models.

This approach significantly enhances multimodal perception by preserving subtle features and overall context, especially for small details that low-resolution models may overlook. The team built a plug-and-play visual tagger based on this foundation and enhanced Griffon v2 with visual language co-reference capabilities, allowing easy interaction with various inputs like coordinates, free text, and flexible target images.

Griffon v2 has proven effective in various tasks such as Reference Expression Generation (REG), phrase localization, and Reference Expression Comprehension (REC). Experimental data shows that the model outperforms expert models in object detection and object counting.

In conclusion, the team summarized their key contributions:

- High-resolution multimodal perception model: By eliminating the need for image segmentation, the model offers an improved approach to local understanding. Its ability to handle resolutions up to 1K has enhanced its ability to capture details.

- Visual-language co-reference structure: An introduced structure that combines language and visual inputs to extend the model's utility and enable various interaction modes. This feature makes communication between users and the model more flexible and natural.

Extensive experiments were conducted to validate the model's effectiveness in various localization tasks. It has achieved state-of-the-art performance in phrase localization, Reference Expression Generation (REG), and Reference Expression Comprehension (REC). The model excels in both quantitative and qualitative object counting, demonstrating its superiority in perception and understanding.

For more information, you can access the project on GitHub here and the research paper on arXiv here.

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