title: Vision language models are blind
summary: Large language models with vision capabilities (VLMs), e.g., GPT-4o and
  Gemini 1.5 Pro are powering countless image-text applications and scoring high
  on many vision-understanding benchmarks. Yet, we find that VLMs fail on 7
  visual tasks absurdly easy to humans such as identifying (a) whether two
  circles overlap; (b) whether two lines intersect; (c) which letter is being
  circled in a word; and (d) counting the number of circles in a Olympic-like
  logo. The shockingly poor performance of four state-of-the-art VLMs suggests
  their vision is, at best, like of a person with myopia seeing fine details as
  blurry, and at worst, like an intelligent person that is blind making educated
  guesses. Code is available at: https://vlmsareblind.github.io/
paper_authors._id: 668de4e71c1fe11f8c1d3c4a
paper_authors.name: Pooyan Rahmanzadehgervi
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paper_authors._id: 668de4e71c1fe11f8c1d3c4b
paper_authors.name: Logan Bolton
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paper_authors.fullname: Logan Bolton
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paper_authors.user: loganbolton
paper_authors._id: 668de4e71c1fe11f8c1d3c4c
paper_authors.name: Mohammad Reza Taesiri
paper_authors.status: extracted_confirmed
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paper_authors.fullname: taesiri
paper_authors.type: user
paper_authors.user: taesiri
paper_authors._id: 668de4e71c1fe11f8c1d3c4d
paper_authors.name: Anh Totti Nguyen
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-07-10T01:33:29.757Z
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paper_authors.fullname: Anh (Totti) Nguyen
paper_authors.type: user
paper_authors.user: anhng8
paper_title: Vision language models are blind
paper_summary: Large language models with vision capabilities (VLMs), e.g., GPT-4o and
  Gemini 1.5 Pro are powering countless image-text applications and scoring high
  on many vision-understanding benchmarks. Yet, we find that VLMs fail on 7
  visual tasks absurdly easy to humans such as identifying (a) whether two
  circles overlap; (b) whether two lines intersect; (c) which letter is being
  circled in a word; and (d) counting the number of circles in a Olympic-like
  logo. The shockingly poor performance of four state-of-the-art VLMs suggests
  their vision is, at best, like of a person with myopia seeing fine details as
  blurry, and at worst, like an intelligent person that is blind making educated
  guesses. Code is available at: https://vlmsareblind.github.io/
paper_ai_summary: State-of-the-art large language models with vision capabilities perform poorly on simple visual tasks, indicating limitations in their visual understanding.
paper_ai_keywords: large language models vision capabilities VLMs GPT-4o Gemini 1.5 Pro visual tasks visual understanding benchmarks myopia visual details blindness educated guesses