Large Vision Language Models have achieved fine-grained object perception, but the limitation of image resolution remains a significant obstacle to surpass the performance of task-specific experts in complex and dense scenarios. Such limitation further restricts the model's potential to achieve nuanced visual and language referring in domains such as GUI Agents, Counting and \etc. To address this issue, we introduce a unified high-resolution generalist model, Griffon v2, enabling flexible object referring with visual and textual prompts. To efficiently scaling up image resolution, we design a simple and lightweight down-sampling projector to overcome the input tokens constraint in Large Language Models. This design inherently preserves the complete contexts and fine details, and significantly improves multimodal perception ability especially for small objects. Building upon this, we further equip the model with visual-language co-referring capabilities through a plug-and-play visual tokenizer. It enables user-friendly interaction with flexible target images, free-form texts and even coordinates. Experiments demonstrate that Griffon v2 can localize any objects of interest with visual and textual referring, achieve state-of-the-art performance on REC, phrase grounding, and REG tasks, and outperform expert models in object detection and object counting. Data, codes and models will be released at https://github.com/jefferyZhan/Griffon.

翻译：大型视觉语言模型已实现细粒度物体感知，但在复杂密集场景中，图像分辨率限制仍是其超越特定任务专家模型性能的主要障碍。该限制进一步制约了模型在图形用户界面代理、计数等场景中实现精细视觉与语言指代的可能性。针对此问题，我们提出统一高分辨率通用模型Griffon v2，支持通过视觉与文本提示进行灵活的物体指代。为高效提升图像分辨率，我们设计了一种简单轻量的下采样投影器，用以突破大型语言模型的输入令牌约束。该设计天然保留了完整上下文与精细细节，显著增强了多模态感知能力，尤其对小物体感知提升显著。在此基础上，我们进一步通过即插即用的视觉分词器赋予模型视觉-语言协同指代能力，支持用户通过灵活的目标图像、自由文本乃至坐标进行交互。实验表明，Griffon v2能够通过视觉与文本指代定位任意感兴趣物体，在REC、短语定位及REG任务中达到最优性能，并在物体检测与计数任务中超越专家模型。数据、代码与模型将发布于https://github.com/jefferyZhan/Griffon。