  Multimodal Large Language Models (MLLMs) have endowed LLMs with the ability
to perceive and understand multi-modal signals. However, most of the existing
MLLMs mainly adopt vision encoders pretrained on coarsely aligned image-text
pairs, leading to insufficient extraction and reasoning of visual knowledge. To
address this issue, we devise a dual-Level vIsual knOwledge eNhanced Multimodal
Large Language Model (LION), which empowers the MLLM by injecting visual
knowledge in two levels. 1) Progressive incorporation of fine-grained
spatial-aware visual knowledge. We design a vision aggregator cooperated with
region-level vision-language (VL) tasks to incorporate fine-grained
spatial-aware visual knowledge into the MLLM. To alleviate the conflict between
image-level and region-level VL tasks during incorporation, we devise a
dedicated stage-wise instruction-tuning strategy with mixture-of-adapters. This
progressive incorporation scheme contributes to the mutual promotion between
these two kinds of VL tasks. 2) Soft prompting of high-level semantic visual
evidence. We facilitate the MLLM with high-level semantic visual evidence by
leveraging diverse image tags. To mitigate the potential influence caused by
imperfect predicted tags, we propose a soft prompting method by embedding a
learnable token into the tailored text instruction. Comprehensive experiments
on several multi-modal benchmarks demonstrate the superiority of our model
(e.g., improvement of 5% accuracy on VSR and 3% CIDEr on TextCaps over
InstructBLIP, 5% accuracy on RefCOCOg over Kosmos-2).
