{"url":"/dataset/illusionanimals-test","name":"IllusionAnimals_test","full_name":null,"description_markdown":"## IllusionAnimals_test  \r\n\r\n### Dataset Characteristics  \r\nIllusionAnimals_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.  \r\n\r\n### Motivations and Content Summary  \r\nThe 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.  \r\n\r\n### Potential Use Cases  \r\n- **Illusory VQA:** Questioning models about the illusions present in the images.  \r\n- **Multimodal Model Evaluation:** Benchmarking multimodal models' ability to interpret and reason about abstract and illusory patterns in naturalistic images.  \r\n- **Perceptual Studies:** Exploring how AI models perceive pareidolia in animal-related visual data.  \r\n- **Synthetic Data Research:** Investigating the use of generated datasets to challenge machine learning models with abstract and complex patterns.","description_withheld":null,"homepage":"https://huggingface.co/datasets/VQA-Illusion/IllusionAnimals_test","introduced_date":"2024-12-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/illusory-vqa-benchmarking-and-enhancing","title":"Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions","first_author":"Mohammadmostafa Rostamkhani","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Visual Question Answering (VQA)","url":"/task/visual-question-answering","datasets_with_task":"/datasets/task/visual-question-answering"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["IllusionAnimals_test"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}