Griffon v2 is a high-resolution multimodal perception model designed to improve object referring via textual and visual cues. It overcomes resolution constraints by introducing a downsampling projector and visual-language co-referring capabilities, resulting in superior performance in tasks like Referring Expression Comprehension and object counting. Experimental data validates its effectiveness, marking a significant advancement in perception and comprehension.
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Griffon v2: A Unified High-Resolution Artificial Intelligence Model Designed to Provide Flexible Object Referring Via Textual and Visual Cues
Large Vision Language Models (LVLMs) like Griffon v2 have shown impressive performance in tasks involving text and image comprehension, particularly in region-level tasks such as Referring Expression Comprehension (REC). These models have advanced perception inside LVLMs, especially in object detection, and have spurred research into improving user interfaces.
Practical Solutions and Value:
Griffon v2 addresses the limitations of LVLMs in complex scenarios by introducing a high-resolution model that provides flexible object referring via textual and visual cues. It overcomes the constraint of picture resolution by incorporating a lightweight downsampling projector, improving multimodal perception abilities and enabling interaction with various inputs.
The model has proven effective in tasks such as Referring Expression Generation (REG), phrase grounding, and Referring Expression Comprehension (REC), outperforming expert models in object detection and counting. Its primary contributions include a high-resolution multimodal perception model and a visual-language co-referring structure, enhancing local understanding and enabling adaptable communication between users and the model.
For middle managers seeking AI solutions, Griffon v2 offers practical benefits in improving perception and comprehension, particularly in tasks involving object detection and counting. Its high-resolution capabilities and flexible interaction modes make it a valuable tool for enhancing user interfaces and multimodal perception.
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