  Do we fully leverage the potential of visual encoder in Multimodal Large
Language Models (MLLMs)? The recent outstanding performance of MLLMs in
multimodal understanding has garnered broad attention from both academia and
industry. In the current MLLM rat race, the focus seems to be predominantly on
the linguistic side. We witness the rise of larger and higher-quality
instruction datasets, as well as the involvement of larger-sized LLMs. Yet,
scant attention has been directed towards the visual signals utilized by MLLMs,
often assumed to be the final high-level features extracted by a frozen visual
encoder. In this paper, we introduce the Dense Connector - a simple, effective,
and plug-and-play vision-language connector that significantly enhances
existing MLLMs by leveraging multi-layer visual features, with minimal
additional computational overhead. Furthermore, our model, trained solely on
images, showcases remarkable zero-shot capabilities in video understanding as
well. Experimental results across various vision encoders, image resolutions,
training dataset scales, varying sizes of LLMs (2.7B->70B), and diverse
architectures of MLLMs (e.g., LLaVA and Mini-Gemini) validate the versatility
and scalability of our approach, achieving state-of-the-art performance on
across 19 image and video benchmarks. We hope that this work will provide
valuable experience and serve as a basic module for future MLLM development.
