IllusionAnimals_test

IllusionAnimals_test

Dataset Characteristics

IllusionAnimals_test is a generated dataset based on a synthetic collection of animal images, including 10 animal classes: cat, dog, pigeon, butterfly, elephant, horse, deer, snake, fish, and rooster. Additionally, it includes a "No Illusion" class, bringing the total number of classes to 11. The dataset contains 1,100 samples, all created synthetically rather than derived from real-world images.

Motivations and Content Summary

The dataset was designed using ControlNet for image generation, with captions provided by four large language models (LLMs). Its purpose is to incorporate the phenomenon of pareidolia—where patterns, often faces, are perceived in random or abstract stimuli—into a variety of animal-related visual contexts. By focusing on animal images, this dataset broadens the scope of illusory reasoning tasks to include more naturalistic and complex visual patterns.

Potential Use Cases

• Illusory VQA: Questioning models about the illusions present in the images.
• Multimodal Model Evaluation: Benchmarking multimodal models' ability to interpret and reason about abstract and illusory patterns in naturalistic images.
• Perceptual Studies: Exploring how AI models perceive pareidolia in animal-related visual data.
• Synthetic Data Research: Investigating the use of generated datasets to challenge machine learning models with abstract and complex patterns.