  Recently, there has been significant interest in enhancing the capability of
multimodal large language models (MLLMs) to process high-resolution images.
Most existing methods focus on adopting a cropping strategy to improve the
ability of multimodal large language models to understand image details.
However, this cropping operation inevitably causes the segmentation of objects
and connected areas, which impairs the MLLM's ability to recognize small or
irregularly shaped objects or text. This issue is particularly evident in
lightweight MLLMs. Addressing this issue, we propose Mini-Monkey, a lightweight
MLLM that incorporates a plug-and-play method called multi-scale adaptive crop
strategy (MSAC). Mini-Monkey adaptively generates multi-scale representations,
allowing it to select non-segmented objects from various scales. To mitigate
the computational overhead introduced by MSAC, we propose a Scale Compression
Mechanism (SCM), which effectively compresses image tokens. Mini-Monkey
achieves state-of-the-art performance among 2B-parameter MLLMs. It not only
demonstrates leading performance on a variety of general multimodal
understanding tasks but also shows consistent improvements in document
understanding capabilities. On the OCRBench, Mini-Monkey achieves a score of
802, outperforming 8B-parameter state-of-the-art model InternVL2-8B. Besides,
our model and training strategy are very efficient, which can be trained with
only eight RTX 3090. The code is available at
https://github.com/Yuliang-Liu/Monkey.
