Honeybee: Locality-enhanced Projector for Multimodal LLM
CoRR(2023)
摘要
In Multimodal Large Language Models (MLLMs), a visual projector plays a
crucial role in bridging pre-trained vision encoders with LLMs, enabling
profound visual understanding while harnessing the LLMs' robust capabilities.
Despite the importance of the visual projector, it has been relatively less
explored. In this study, we first identify two essential projector properties:
(i) flexibility in managing the number of visual tokens, crucial for MLLMs'
overall efficiency, and (ii) preservation of local context from visual
features, vital for spatial understanding. Based on these findings, we propose
a novel projector design that is both flexible and locality-enhanced,
effectively satisfying the two desirable properties. Additionally, we present
comprehensive strategies to effectively utilize multiple and multifaceted
instruction datasets. Through extensive experiments, we examine the impact of
individual design choices. Finally, our proposed MLLM, Honeybee, remarkably
outperforms previous state-of-the-art methods across various benchmarks,
including MME, MMBench, SEED-Bench, and LLaVA-Bench, achieving significantly
higher efficiency. Code and models are available at
https://github.com/kakaobrain/honeybee.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要