title: VoCo-LLaMA: Towards Vision Compression with Large Language Models
summary: 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}.
paper_authors._id: 66724f16cec66c5343fb5915
paper_authors.name: Xubing Ye
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-19T07:38:01.036Z
paper_authors._id: 6584179da76333af9f61e90b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6584179da76333af9f61e90b/J63cWvnALEddg42h1p3Ew.jpeg
paper_authors.fullname: Xubing Ye
paper_authors.type: user
paper_authors.user: YxxxB
paper_authors._id: 66724f16cec66c5343fb5916
paper_authors.name: Yukang Gan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-19T08:37:45.720Z
paper_authors._id: 648b0caa6d6741453167c589
paper_authors.avatarUrl: /avatars/e46fba100dede1f0afbf615154b4e63d.svg
paper_authors.fullname: YukangGan
paper_authors.type: user
paper_authors.user: ganyk
paper_authors._id: 66724f16cec66c5343fb5917
paper_authors.name: Xiaoke Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-19T07:37:49.199Z
paper_authors._id: 63318b2349a9563915469f3b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63318b2349a9563915469f3b/zlbeB2997i8YkoyOTb9FL.jpeg
paper_authors.fullname: Xiaoke Huang
paper_authors.type: user
paper_authors.user: xk-huang
paper_authors._id: 66724f16cec66c5343fb5918
paper_authors.name: Yixiao Ge
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-19T08:37:51.818Z
paper_authors._id: 640e9762b03f4cd29f58d982
paper_authors.avatarUrl: /avatars/81da37d628163fe3e094b247c7c3a3b5.svg
paper_authors.fullname: Yixiao Ge
paper_authors.type: user
paper_authors.user: yxgeee
paper_authors._id: 66724f16cec66c5343fb5919
paper_authors.name: Ying Shan
paper_authors._id: 66724f16cec66c5343fb591a
paper_authors.name: Yansong Tang
paper_title: VoCo-LLaMA: Towards Vision Compression with Large Language Models
paper_summary: 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}.
paper_ai_keywords: Vision-Language Models (VLMs) multi-modal tasks context window computational cost high-resolution image inputs videos vision compression vision tokens external modules compression learning process VoCo-LLaMA vision instruction tuning phase attention distillation inference stage FLOPs (Floating Point Operations Per Second) inference time temporal correlations video question-answering benchmarks