Despite remarkable progress, existing multimodal large language models
(MLLMs) are still inferior in granular visual recognition. Contrary to previous
works, we study this problem from the perspective of image resolution, and
reveal that a combination of low- and high-resolution visual features can
effectively mitigate this shortcoming. Based on this observation, we propose a
novel and efficient method for MLLMs, termed Mixture-of-Resolution Adaptation
(MRA). In particular, MRA adopts two visual pathways for images with different
resolutions, where high-resolution visual information is embedded into the
low-resolution pathway via the novel mixture-of-resolution adapters
(MR-Adapters). This design also greatly reduces the input sequence length of
MLLMs. To validate MRA, we apply it to a recent MLLM called LLaVA, and term the
new model LLaVA-HR. We conduct extensive experiments on 11 vision-language (VL)
tasks, which show that LLaVA-HR outperforms existing MLLMs on 8 VL tasks, e.g.,
+9.4% on TextVQA. More importantly, both training and inference of LLaVA-HR
remain efficient with MRA, e.g., 20 training hours and 3$\times$ inference
speed than LLaVA-1.5. Source codes are released at:
this https URL

基于图像分辨率的新型多模态大语言模型方法 (LLaVA-HR) 通过采用低分辨率和高分辨率图像特征的组合有效地改善了视觉识别的问题，在 11 个视觉 - 语言任务中表现出比现有模型更好的性能。