In their paper titled "Vision language models are blind," authors Pooyan Rahmanzadehgervi, Logan Bolton, Mohammad Reza Taesiri, and Anh Totti Nguyen highlight the limitations of large language models with vision capabilities (VLMs) such as GPT-4o and Gemini 1.5 Pro. These models excel in many image-text applications and vision-understanding benchmarks but struggle with basic visual tasks that are trivial for humans. The authors conducted a study to assess the performance of four state-of-the-art VLMs on these visual tasks and found them to be shockingly poor. They likened the vision capabilities of these models to that of a person with myopia who sees fine details as blurry or even to an intelligent person making educated guesses while being blind. The code for their study is available at https://vlmsareblind.github.io/. Additionally, the authors discuss existing benchmarks used to evaluate VLMs' vision understanding abilities. These include assessing performance on college-level topics, charts, documents, or videos. While these benchmarks have shown rapid progress in VLMs' performance, they often rely on real-world data that require extensive prior knowledge and can suffer from data leakage issues where VLMs can provide accurate answers without even needing the input image. To address these limitations, the authors propose a new benchmark called BlindTest that focuses on low-level visual tasks that are extremely easy for humans and do not require prior knowledge or complex reasoning. This benchmark aims to provide a more accurate assessment of VLMs' vision capabilities by testing their ability to perceive images like humans do without relying heavily on language processing. Overall, this study sheds light on the challenges faced by current VLMs in basic visual tasks and proposes a new benchmark to better evaluate their vision understanding abilities.
- Authors highlight limitations of large language models with vision capabilities (VLMs) like GPT-4o and Gemini 1.5 Pro
- VLMs excel in image-text applications but struggle with basic visual tasks
- Study found state-of-the-art VLMs performed poorly on visual tasks
- Vision capabilities of models likened to a person with myopia or an intelligent person making educated guesses while blind
- Authors propose new benchmark called BlindTest for low-level visual tasks without prior knowledge or complex reasoning
- Big language models with vision abilities like GPT-4o and Gemini 1.5 Pro have some problems, as authors say.
- These models are good at using images with text but have trouble with simple visual jobs.
- A study discovered that the best VLMs do badly on visual tasks.
- People compare the vision skills of these models to a nearsighted person or a smart person guessing without seeing.
- Authors suggest a new test called BlindTest for basic visual tasks without knowing things beforehand or thinking too hard.
Definitions
- Authors: People who write books, articles, or studies.
- Limitations: Things that hold back or restrict something from being perfect.
- Visual capabilities: The ability to see and understand images or things you can look at.
- Excel: To be really good at something.
- Struggle: To find something difficult or challenging.
- State-of-the-art: The most advanced and up-to-date technology available.
- Likened: Compared to; said something is similar to another thing in some way.
- Myopia: A condition where someone has trouble seeing things clearly far away (like nearsightedness).
- Educated guesses: Making smart guesses based on what you know or think is likely true.
- Benchmark: A standard used for comparing how well something performs against others in the same category.
- Prior knowledge: Information you already know before starting something new.
- Complex reasoning: Thinking deeply about a problem or
Introduction
In recent years, there has been a significant advancement in language models with vision capabilities (VLMs) such as GPT-40 and Gemini 1.5 Pro. These models have shown impressive performance in image-text applications and vision-understanding benchmarks. However, a recent study by Pooyan Rahmanzadehgervi et al., titled "Vision language models are blind," highlights the limitations of these VLMs when it comes to basic visual tasks.
The authors conducted a comprehensive study to assess the performance of four state-of-the-art VLMs on various visual tasks and found them to be surprisingly poor. They compared the vision capabilities of these models to that of a person with myopia or even an intelligent person making educated guesses while being blind. This research paper sheds light on the challenges faced by current VLMs in basic visual tasks and proposes a new benchmark called BlindTest to better evaluate their vision understanding abilities.
Limitations of Current VLMs
The authors first discuss the limitations of current VLMs when it comes to basic visual tasks. They argue that while these models excel in complex image-text applications, they struggle with simple visual tasks that are trivial for humans. The researchers conducted experiments on four popular VLMs – GPT-40, Gemini 1.5 Pro, CLIP, and ViLBERT – and found them to perform poorly on low-level visual tasks such as object detection, scene recognition, and spatial reasoning.
To illustrate this point further, they provide examples where these models fail at basic image understanding tasks like identifying objects in images or recognizing simple geometric shapes. The authors attribute this limitation to the fact that current VLMs rely heavily on text processing rather than pure image processing.
Existing Benchmarks for Evaluating VLM Vision Capabilities
Next, the paper discusses existing benchmarks used for evaluating VLM's vision understanding abilities. These include assessing performance on college-level topics, charts, documents, or videos. While these benchmarks have shown rapid progress in VLMs' performance, they often suffer from data leakage issues.
Data leakage refers to a situation where the model can provide accurate answers without even needing the input image. This is because these benchmarks rely on real-world data that require extensive prior knowledge and complex reasoning. As a result, current VLMs may perform well on these benchmarks but still struggle with basic visual tasks.
Introducing BlindTest
To address the limitations of existing benchmarks and provide a more accurate assessment of VLMs' vision capabilities, the authors propose a new benchmark called BlindTest. This benchmark focuses on low-level visual tasks that are extremely easy for humans and do not require prior knowledge or complex reasoning.
BlindTest aims to evaluate VLMs' ability to perceive images like humans do by testing their understanding of simple visual concepts such as shapes, colors, and spatial relationships. The authors believe that this benchmark will provide a better understanding of VLM's true vision capabilities without relying heavily on language processing.
Conclusion
In conclusion, "Vision language models are blind" highlights the limitations of current VLMs when it comes to basic visual tasks and proposes a new benchmark called BlindTest to better evaluate their vision understanding abilities. The paper sheds light on the challenges faced by current VLMs in image processing and emphasizes the need for further research in this area.
The code for this study is available at https://vlmsareblind.github.io/, allowing other researchers to replicate and build upon these findings. With BlindTest as a new benchmark for evaluating VLM's vision capabilities, we can expect more accurate assessments of these models' performance in future studies. This research has significant implications for improving current VLMs' vision capabilities and advancing our understanding of artificial intelligence's potential in image processing tasks.
