This article introduces the latest high-resolution AI model Griffon v2. The model combines textual and visual cues, enables flexible object referencing, and enhances multimodal perception through downsampling projectors. In tasks such as reference expression generation, phrase positioning, and reference expression understanding, Griffon v2 outperforms expert models, especially showing significant advantages in visual-linguistic coreference structure, target detection, and object counting. Its emergence marks important progress in multi-modal understanding and application of AI models.
The latest high-resolution AI model, Griffon v2, combines textual and visual cues to provide flexible object referencing. The team used downsampling projectors to enhance multi-modal perception capabilities. The model performs well in quotation expression generation, phrase localization, and quotation expression understanding tasks, outperforming expert models. It has a visual-linguistic coreference structure and shows superiority in target detection and object counting.
The Griffon v2 model's breakthrough in multi-modal understanding provides broader possibilities for future AI applications. Its superior performance in target detection and object counting also indicates its huge potential in practical applications. It is believed that more innovative applications based on this model will appear in the future.