EE-MLLM: A Data-Efficient and Compute-Efficient Multimodal Large Language Model

Understand

Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated satisfactory performance across various vision-language tasks.

• Current approaches for vision and language interaction fall into two categories: self-attention-based and cross-attention-based methods.
• However, both approaches present inherent limitations, forcing a trade-off between data and computational efficiency.
• To address this issue, we introduce the Data-$\textbf{E}$fficient and Compute-$\textbf{E}$fficient $\textbf{MLLM}$ ($\textbf{EE-MLLM}$).