title: UnifiedVisionGPT: Streamlining Vision-Oriented AI through Generalized
    Multimodal Framework
summary: In the current landscape of artificial intelligence, foundation models serve
  as the bedrock for advancements in both language and vision domains. OpenAI
  GPT-4 has emerged as the pinnacle in large language models (LLMs), while the
  computer vision (CV) domain boasts a plethora of state-of-the-art (SOTA) models
  such as Meta's SAM and DINO, and YOLOS. However, the financial and
  computational burdens of training new models from scratch remain a significant
  barrier to progress. In response to this challenge, we introduce
  UnifiedVisionGPT, a novel framework designed to consolidate and automate the
  integration of SOTA vision models, thereby facilitating the development of
  vision-oriented AI. UnifiedVisionGPT distinguishes itself through four key
  features: (1) provides a versatile multimodal framework adaptable to a wide
  range of applications, building upon the strengths of multimodal foundation
  models; (2) seamlessly integrates various SOTA vision models to create a
  comprehensive multimodal platform, capitalizing on the best components of each
  model; (3) prioritizes vision-oriented AI, ensuring a more rapid progression in
  the CV domain compared to the current trajectory of LLMs; and (4) introduces
  automation in the selection of SOTA vision models, generating optimal results
  based on diverse multimodal inputs such as text prompts and images. This paper
  outlines the architecture and capabilities of UnifiedVisionGPT, demonstrating
  its potential to revolutionize the field of computer vision through enhanced
  efficiency, versatility, generalization, and performance. Our implementation,
  along with the unified multimodal framework and comprehensive dataset, is made
  publicly available at https://github.com/LHBuilder/SA-Segment-Anything.
paper_authors._id: 655abcb93ff5ba1b1b3a644e
paper_authors.name: Chris Kelly
paper_authors._id: 655abcb93ff5ba1b1b3a644f
paper_authors.name: Luhui Hu
paper_authors._id: 655abcb93ff5ba1b1b3a6450
paper_authors.name: Cindy Yang
paper_authors._id: 655abcb93ff5ba1b1b3a6451
paper_authors.name: Yu Tian
paper_authors._id: 655abcb93ff5ba1b1b3a6452
paper_authors.name: Deshun Yang
paper_authors._id: 655abcb93ff5ba1b1b3a6453
paper_authors.name: Bang Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-20T13:52:13.303Z
paper_authors._id: 63e32fbac123238b18a06d5e
paper_authors.avatarUrl: /avatars/a6fab9c4430c7616ce050dab5b819f7e.svg
paper_authors.fullname: Bang Yang
paper_authors.type: user
paper_authors.user: yangbang18
paper_authors._id: 655abcb93ff5ba1b1b3a6454
paper_authors.name: Zaoshan Huang
paper_authors._id: 655abcb93ff5ba1b1b3a6455
paper_authors.name: Zihao Li
paper_authors._id: 655abcb93ff5ba1b1b3a6456
paper_authors.name: Yuexian Zou
paper_title: UnifiedVisionGPT: Streamlining Vision-Oriented AI through Generalized
    Multimodal Framework
paper_summary: In the current landscape of artificial intelligence, foundation models serve
  as the bedrock for advancements in both language and vision domains. OpenAI
  GPT-4 has emerged as the pinnacle in large language models (LLMs), while the
  computer vision (CV) domain boasts a plethora of state-of-the-art (SOTA) models
  such as Meta's SAM and DINO, and YOLOS. However, the financial and
  computational burdens of training new models from scratch remain a significant
  barrier to progress. In response to this challenge, we introduce
  UnifiedVisionGPT, a novel framework designed to consolidate and automate the
  integration of SOTA vision models, thereby facilitating the development of
  vision-oriented AI. UnifiedVisionGPT distinguishes itself through four key
  features: (1) provides a versatile multimodal framework adaptable to a wide
  range of applications, building upon the strengths of multimodal foundation
  models; (2) seamlessly integrates various SOTA vision models to create a
  comprehensive multimodal platform, capitalizing on the best components of each
  model; (3) prioritizes vision-oriented AI, ensuring a more rapid progression in
  the CV domain compared to the current trajectory of LLMs; and (4) introduces
  automation in the selection of SOTA vision models, generating optimal results
  based on diverse multimodal inputs such as text prompts and images. This paper
  outlines the architecture and capabilities of UnifiedVisionGPT, demonstrating
  its potential to revolutionize the field of computer vision through enhanced
  efficiency, versatility, generalization, and performance. Our implementation,
  along with the unified multimodal framework and comprehensive dataset, is made
  publicly available at https://github.com/LHBuilder/SA-Segment-Anything.
paper_ai_summary: UnifiedVisionGPT is a novel framework that consolidates and automates the integration of state-of-the-art vision models, enhancing computer vision through efficiency, versatility, generalization, and performance.
paper_ai_keywords: foundation models large language models multimodal framework state-of-the-art vision models computer vision multimodal platform vision-oriented AI automation SOTA vision models SOTA