README.md

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<h1>IllusionBench: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models</h1>
 

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    Yiming Zhang <sup>1</sup>
    <a href="https://zzc-1998.github.io/" target="_blank">Zicheng Zhang</a><sup>1</sup>,
    Wei Xinyi <sup>1</sup>
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    <a href="https://scholar.google.ca/citations?user=Tq2hoMQAAAAJ&hl=en" target="_blank">Xiaohong Liu</a><sup>1</sup>,
    <a href="https://ee.sjtu.edu.cn/en/FacultyDetail.aspx?id=24&infoid=153&flag=153" target="_blank">Guangtao Zhai</a><sup>1</sup>, 
    <a href="https://minxiongkuo.github.io/" target="_blank">Xiongkuo Min</a><sup>1</sup><sup>#</sup>
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  <sup>1</sup>Shanghai Jiaotong University
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    <sup>#</sup>Corresponding author
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We introduce **IllusionBench**, the first benchmark designed to evaluate the capability of Vision-Language Models 
in understanding **visual illusions**.   

**IllusionBench** is a large-scale visual illusion dataset, encompassing 1k+ images, 5k+ question-answer pairs, 
and 1k+ golden text descriptions, covering the presence, causes, and content of illusions.    

We benchmark the performance of ten state-of-the-art VLMs on this dataset, evaluating their understanding of illusion 
images through true-or-false, multiple-choice, and open-ended descriptive tasks.  

## IllusionBench Construction
**IllusionBench** includes 1K+ images from various online repositories. The detailed distribution is shown below.  

1. **Classic Cognitive Illusion:**  These include blur, distortion, paradox, and fictitious illusions—key 
examples of traditional synthetic illusions. Designed by psychologists, these ambiguous images test VLMs' 
alignment with human perception.
2. **Trap Illusion:** Trap illusions are edited versions of classic visual illusions, resembling them in appearance 
but differing in physical properties.
3. **Real Scene Illusion:** These images depict real-world objects and scenes, with unique and definite 
semantic descriptions.
4. **Ishihara Color Blindness Detection:** Ishihara images, verified by vision-healthy individuals, 
where the patterns convey unique and definite semantics.
5. **No Illusion:** Depicting diverse subjects such as people, landscapes, and objects.
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**IllusionBench** including three question types:
1. **True-or-false:** Over 2k binary questions focused on semantic content and the presence of illusions.
2. **Multiple-choice:** Over 3k multiple-choice questions targeting fine-grained perception of image content 
and illusion causes.
3. **Open-ended Description:** Each image is accompanied by a manually crafted **golden description**, 
covering the main content of the image, the existence of any visual illusion, and the causes of the illusion.

## Glance at IllusionBench Performance
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Performance of advanced VLMs and human evaluators on IllusionBench+ perception tasks (left) and description tasks 
(right). The left image shows P1-P5 representing perception tasks on the subsets of **Classic Cognitive Illusion, Real 
Scene Illusion, No Illusion, Ishihara Image, and Trap Illusion**, respectively. Similarly, the right image shows D1-D5 
representing description tasks on these subsets. "T/F", "Mul", "Sem", and "Illu" respectively represent true-or-
false, multiple-choice, semantic descriptions, and illusion descriptions.
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## Evaluate your model on IllusionBench
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Assume that you have download the [**IllusionBench**](https://huggingface.co/datasets/MingZhangSJTU/IllusionBench)

We provide a sample `Closed_inference.py` for testing Qwen-vl-max API format on **IllusionBench** true-or-false question
and multiple-choice question. 

We provide a sample `Opened_inference.py` for testing Qwen-vl-max API format on **IllusionBench** semantic and illusion 
description question. 

You can modify the code to test your own model.
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## Contact

Please contact any of the first authors of this paper for queries.

- Yiming Zhang, `ming_zhang_sjtu@sjtu.edu.cn`, Shanghai Jiao Tong University