  Multi-modal large language models (MLLMs) have made significant strides in
various visual understanding tasks. However, the majority of these models are
constrained to process low-resolution images, which limits their effectiveness
in perception tasks that necessitate detailed visual information. In our study,
we present MG-LLaVA, an innovative MLLM that enhances the model's visual
processing capabilities by incorporating a multi-granularity vision flow, which
includes low-resolution, high-resolution, and object-centric features. We
propose the integration of an additional high-resolution visual encoder to
capture fine-grained details, which are then fused with base visual features
through a Conv-Gate fusion network. To further refine the model's object
recognition abilities, we incorporate object-level features derived from
bounding boxes identified by offline detectors. Being trained solely on
publicly available multimodal data through instruction tuning, MG-LLaVA
demonstrates exceptional perception skills. We instantiate MG-LLaVA with a wide
variety of language encoders, ranging from 3.8B to 34B, to evaluate the model's
performance comprehensively. Extensive evaluations across multiple benchmarks
demonstrate that MG-LLaVA outperforms existing MLLMs of comparable parameter
sizes, showcasing its remarkable efficacy. The code will be available at
https://github.com/PhoenixZ810/MG-LLaVA.
