VoCo-LLaMA: Towards Vision Compression with Large Language Models
Vision-Language Models (VLMs) have achieved remarkable success in various
multi-modal tasks, but they are often bottlenecked by the limited context
window and high computational cost of processing high-resolution image inputs
and videos. Vision compression can alleviate this problem by reducing the
vision token count. Previous approaches compress vision tokens with external
modules and force LLMs to understand the compressed ones, leading to visual
information loss. However, the LLMs' understanding paradigm of vision tokens is
not fully utilised in the compression learning process. We propose VoCo-LLaMA,
the first approach to compress vision tokens using LLMs. By introducing Vision
Compression tokens during the vision instruction tuning phase and leveraging
attention distillation, our method distill how LLMs comprehend vision tokens
into their processing of VoCo tokens. VoCo-LLaMA facilitates effective vision
compression and improves the computational efficiency during the inference
stage. Specifically, our method achieves minimal performance loss with a
compression ratio of 576times, resulting in up to 94.8% fewer FLOPs and
69.6% acceleration in inference time. Furthermore, through continuous
training using time-series compressed token sequences of video frames,
VoCo-LLaMA demonstrates the ability to understand temporal correlations,
outperforming previous methods on popular video question-answering benchmarks.
Our approach presents a promising way to unlock the full potential of VLMs'
contextual window, enabling more scalable multi-modal applications. The project
page, along with the associated code, can be accessed via
https://yxxxb.github.io/VoCo-LLaMA-page/{this https URL}.
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Xubing Ye
claimed_verified
2024-06-19T07:38:01.036Z
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Xubing Ye
user
YxxxB
66724f16cec66c5343fb5916
Yukang Gan
admin_assigned
2024-06-19T08:37:45.720Z
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YukangGan
user
ganyk
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Xiaoke Huang
claimed_verified
2024-06-19T07:37:49.199Z
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Xiaoke Huang
user
xk-huang
66724f16cec66c5343fb5918
Yixiao Ge
admin_assigned
2024-06-19T08:37:51.818Z
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Yixiao Ge
user
yxgeee
66724f16cec66c5343fb5919
Ying Shan
66724f16cec66c5343fb591a
Yansong Tang
VoCo-LLaMA: Towards Vision Compression with Large Language Models
Vision-Language Models (VLMs) have achieved remarkable success in various
multi-modal tasks, but they are often bottlenecked by the limited context
window and high computational cost of processing high-resolution image inputs
and videos. Vision compression can alleviate this problem by reducing the
vision token count. Previous approaches compress vision tokens with external
modules and force LLMs to understand the compressed ones, leading to visual
information loss. However, the LLMs' understanding paradigm of vision tokens is
not fully utilised in the compression learning process. We propose VoCo-LLaMA,
the first approach to compress vision tokens using LLMs. By introducing Vision
Compression tokens during the vision instruction tuning phase and leveraging
attention distillation, our method distill how LLMs comprehend vision tokens
into their processing of VoCo tokens. VoCo-LLaMA facilitates effective vision
compression and improves the computational efficiency during the inference
stage. Specifically, our method achieves minimal performance loss with a
compression ratio of 576times, resulting in up to 94.8% fewer FLOPs and
69.6% acceleration in inference time. Furthermore, through continuous
training using time-series compressed token sequences of video frames,
VoCo-LLaMA demonstrates the ability to understand temporal correlations,
outperforming previous methods on popular video question-answering benchmarks.
Our approach presents a promising way to unlock the full potential of VLMs'
contextual window, enabling more scalable multi-modal applications. The project
page, along with the associated code, can be accessed via
https://yxxxb.github.io/VoCo-LLaMA-page/{this https URL}.
VoCo-LLaMA, a vision token compression method using LLMs, enhances computational efficiency and maintains performance in VLMs, facilitating scalable multi-modal applications.
vision token compression LLMs vision instruction tuning attention distillation VoCo-LLaMA FLOPs inference time video question-answering temporal correlations