Honeybee: Locality-enhanced Projector for Multimodal LLM
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.
65791a18c17736d75f7d6e42
Junbum Cha
admin_assigned
2023-12-13T09:40:07.151Z
641fee15b286e8c464f14f10
/avatars/dbfb6ac17156086df2408703f325881e.svg
Junbum Cha
user
khanrc
65791a18c17736d75f7d6e43
Wooyoung Kang
admin_assigned
2023-12-13T09:40:16.303Z
64b785c84df206a3ed1443ba
/avatars/141d4ae55f604c54eadd4aacb4e29514.svg
Woo-young Kang
user
Edwin-kang
65791a18c17736d75f7d6e44
Jonghwan Mun
admin_assigned
2023-12-13T09:40:23.331Z
64c9cc003a3f428da6927587
/avatars/05daf4190b3c96b7ef4c5c9fe78697c3.svg
Jonghwan Mun
user
jhmun
65791a18c17736d75f7d6e45
Byungseok Roh
Honeybee: Locality-enhanced Projector for Multimodal LLM
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.
A novel visual projector for Multimodal Large Language Models enhances flexibility and locality, leading to improved performance across various benchmarks.
Multimodal Large Language Models visual projector vision encoders visual tokens local context projector design instruction datasets MME MMBench SEED-Bench LLaVA-Bench