  Large Multimodal Models (LMMs) have shown promise in vision-language tasks
but struggle with high-resolution input and detailed scene understanding.
Addressing these challenges, we introduce Monkey to enhance LMM capabilities.
Firstly, Monkey processes input images by dividing them into uniform patches,
each matching the size (e.g., 448x448) used in the original training of the
well-trained vision encoder. Equipped with individual adapter for each patch,
Monkey can handle higher resolutions up to 1344x896 pixels, enabling the
detailed capture of complex visual information. Secondly, it employs a
multi-level description generation method, enriching the context for
scene-object associations. This two-part strategy ensures more effective
learning from generated data: the higher resolution allows for a more detailed
capture of visuals, which in turn enhances the effectiveness of comprehensive
descriptions. Extensive ablative results validate the effectiveness of our
designs. Additionally, experiments on 18 datasets further demonstrate that
Monkey surpasses existing LMMs in many tasks like Image Captioning and various
Visual Question Answering formats. Specially, in qualitative tests focused on
dense text question answering, Monkey has exhibited encouraging results
compared with GPT4V. Code is available at
https://github.com/Yuliang-Liu/Monkey.
