  In the field of multi-modal language models, the majority of methods are
built on an architecture similar to LLaVA. These models use a single-layer ViT
feature as a visual prompt, directly feeding it into the language models
alongside textual tokens. However, when dealing with long sequences of visual
signals or inputs such as videos, the self-attention mechanism of language
models can lead to significant computational overhead. Additionally, using
single-layer ViT features makes it challenging for large language models to
perceive visual signals fully. This paper proposes an efficient multi-modal
language model to minimize computational costs while enabling the model to
perceive visual signals as comprehensively as possible. Our method primarily
includes: (1) employing cross-attention to image-text interaction similar to
Flamingo. (2) utilize hierarchical ViT features. (3) introduce the Mixture of
Experts (MoE) mechanism to enhance model effectiveness. Our model achieves
competitive scores on public multi-modal benchmarks and performs well in tasks
such as image captioning and video captioning.
