csebuetnlp/illusionVQA-Soft-Localization
IllusionVQA: Optical Illusion Dataset Project Page | Paper | Github TL;DR IllusionVQA is a dataset of optical illusions and hard-to-interpret scenes designed to test the capability of Vision Language Models in comprehension and soft localization tasks. GPT4V achieved 62.99% accuracy on comprehension and 49.7% on localization, while humans achieved 91.03% and 100% respectively. Usage from datasets import load_dataset import base64 from openai import… See the full description on the dataset page: https://huggingface.co/datasets/csebuetnlp/illusionVQA-Soft-Localization.
| Author / organization | csebuetnlp |
|---|---|
| License (as declared) | cc-by-nc-sa-4.0 |
| Downloads | 170 (as reported by Hugging Face) |
| Likes | 4 |
| Created | 16 March 2024 |
| Last updated at source | 26 March 2024 |
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170 now (+12 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Recent changes
- 16 Oct 2026 Description: IllusionVQA: Optical Illusion Dataset Project Page | Paper | Github TL;DR IllusionVQA is a dataset of optical illusions and hard-to-interpret scenes designed to test the capability of Vision Language Models in comprehension and soft localiz → IllusionVQA: Optical Illusion Dataset Project Page | Paper | Github TL;DR IllusionVQA is a dataset of optical illusions and hard-to-interpret scenes designed to test the capability of Vision Language Models in comprehension and soft localiz
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